Emory Law Journal

Student Surveillance, Racial Inequalities, and Implicit Racial Bias
Jason P. Nance Associate Professor of Law, University of Florida Levin College of Law. J.D., University of Pennsylvania Law School; Ph.D., M.A., Educational Administration, The Ohio State University. I would like to thank the following individuals for the helpful feedback and assistance they provided for this Article: Nancy Dowd, Barry Feld, Michael Heise, Darren Hutchinson, Julie Nance, Sharon Rush, Katheryn Russell-Brown, Chris Slobogin, Greg Stein, John Stinneford, Michael Allan Wolf, and David Wolitz. I also appreciate the helpful comments provided by faculty members and conference participants during presentations of earlier versions of this work at the University of Tennessee College of Law and the Present and Future of Civil Rights Movements Conference at Duke Law School. Finally, I am also grateful for the excellent research assistance I received from Samanta Franchim, Anthony Kakoyannis, and Laura Liles.

Abstract

In the wake of high-profile incidents of school violence, school officials have increased their reliance on a host of surveillance measures to maintain order and control in their schools. Paradoxically, such practices can foster hostile environments that may lead to even more disorder and dysfunction. These practices may also contribute to the so-called “school-to-prison pipeline” by pushing more students out of school and into the juvenile justice system. However, not all students experience the same level of surveillance. This Article presents data on school surveillance practices, including an original empirical analysis of restricted data recently released by the U.S. Department of Education after the shootings at Sandy Hook Elementary School. Paralleling other disturbing trends of inequality in our public school system, these results and other empirical analyses reveal that schools serving primarily students of color are more likely to rely on more intense surveillance measures than other schools. Further, the empirical evidence suggests that these racial disparities may not be justified by legitimate safety concerns. This Article then turns to a discussion of the role that implicit racial bias may have in school officials’ decisions to rely on intense surveillance methods. Finally, it proposes legislation and strategies that federal lawmakers, state lawmakers, and school officials should adopt to counteract the effect of implicit racial bias on school officials’ decisions to implement strict security measures (and other decisions school officials make). Implementing these recommendations will help create better learning environments that benefit students of all races.

Introduction 1This Article builds upon my prior works on student surveillance, school security, the “school-to-prison pipeline,” racial inequalities, and implicit racial bias, particularly Jason P. Nance, Dismantling the School-to-Prison Pipeline: Tools for Change, 48 Ariz. St. L.J. 313 (2016) [hereinafter Nance, Dismantling the School-to-Prison Pipeline]; Jason P. Nance, Over-Disciplining Students, Racial Bias, and the School-to-Prison Pipeline, 50 U. Rich. L. Rev. 1063 (2016) [hereinafter Nance, Over-Disciplining Students]; Jason P. Nance, Random, Suspicionless Searches of Students’ Belongings: A Legal, Empirical, and Normative Analysis, 84 U. Colo. L. Rev. 367 (2013) [hereinafter Nance, Random, Suspicionless Searches]; Jason P. Nance, School Surveillance and the Fourth Amendment, 2014 Wis. L. Rev. 79 (2014) [hereinafter Nance, School Surveillance and the Fourth Amendment]; Jason P. Nance, Students, Police, and the School-to-Prison Pipeline, 93 Wash. U. L. Rev. 919 (2016); and Jason P. Nance, Students, Security, and Race, 63 Emory L.J. 1 (2013). Accordingly, to fully understand the significance of the theories, empirical analyses, and proposals for reform I present here, I draw upon, summarize, and highlight in this Article certain material discussed in my prior works for the reader’s convenience.

More than sixty years after the landmark decision in Brown v. Board of Education, 2347 U.S. 483 (1954). stark racial inequalities persist in our public education system. The disparate treatment of minority students has been documented repeatedly in almost all areas of public education. For example, it is more common for students of color, especially low-income students of color, to be in overcrowded classrooms, attend schools in deplorable physical condition, and be taught by educators who are less experienced, less credentialed, and lower paid. 3See, e.g., Gary Orfield & Chungmei Lee, Racial Transformation and the Changing Nature of Segregation 29–31 (2006) (describing inequalities in our public education system, especially with respect to minority students living in concentrated poverty); Robert D. Putnam, Our Kids: The American Dream in Crisis 135–90 (2015) (describing the inequalities and disparate opportunities that exist in our educational system); Linda Darling-Hammond, Inequality and School Resources: What It Will Take to Close the Opportunity Gap, in Closing the Opportunity Gap: What America Must Do to Give Every Child an Even Chance 77, 77–97 (Prudence L. Carter & Kevin G. Welner eds., 2013) (describing the inequalities present in our education system); Jason P. Nance, Persisting Inequalities in Educational Resources and Results: A Call for Reform, in The Road to Progress: The Case for a U.S. Education Amendment (Charles J. Ogletree, Jr. & Kimberly Jenkins Robinson eds., forthcoming) [hereinafter Nance, Persisting Inequalities] (describing the inequalities that persist in our public education system, especially with respect to low-income minority students); Kimberly Jade Norwood, Blackthink’s™ Acting White Stigma in Education and How It Fosters Academic Paralysis in Black Youth, 50 How. L.J. 711, 727–28 (2007) (describing many of the schools that black youth attend as “grossly underfunded”); Gary Orfield, The Growth of Segregation: African Americans, Latinos, and Unequal Education, in Dismantling Desegregation: The Quiet Reversal of Brown v. Board of Education 53, 69 (Gary Orfield & Susan E. Eaton eds., 1996) (“[D]isadvantaged students face more barriers and receive less reinforcement to succeed in school.”); Meredith Phillips & Tiffani Chin, School Inequality: What Do We Know?, in Social Inequality 467, 484–85 (Kathryn Neckerman ed., 2004) (describing class size inequalities); Press Release, U.S. Dep’t of Educ., New Data from U.S. Department of Education Highlights Educational Inequalities Around Teacher Experience, Discipline and High School Rigor (Mar. 6, 2012), http://www.ed.gov/news/press-releases/new-data-us-department-education-highlights-educational-inequities-around-teache (describing the inequalities that exist for minorities in our public education system); see generally Jonathan Kozol, Savage Inequalities: Children in America’s Schools (1991) (examining the racial segregation and inequality persisting in many schools and ways to resolve the problem based on children’s desires). They are more likely to be suspended, expelled, referred to law enforcement, or subject to a school-based arrest than similarly situated white students. 4See Letter from Catherine E. Lhamon, Assistant Sec’y, Office for Civil Rights, U.S. Dep’t of Educ., & Jocelyn Samuels, Acting Assistant Attorney Gen., Civil Rights Div., U.S. Dep’t of Justice, to Colleague, Dear Colleague Letter on the Nondiscriminatory Administration of School Discipline 3–5 (Jan. 8, 2014) [hereinafter Dear Colleague Letter], http://www2.ed.gov/about/offices/list/ocr/letters/colleague-201401-title-vi.pdf; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 331–36; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 957. They have less access to counselors, gifted and talented programs, music and art curricula, project-based science classes, extra-curricular activities, and higher-level science and mathematics courses. 5See U.S. Gov’t Accountability Office, GAO-16-345, K–12 Education: Better Use of Information Could Help Agencies Identify Disparities and Address Racial Discrimination 21–22 (2016); Office of Civil Rights, U.S. Dep’t of Educ., 2013–2014 Civil Rights Data Collection: A First Look 6–7 (2016); Office of Civil Rights, U.S. Dep’t of Educ., Protecting Civil Rights, Advancing Equity 16–17 (2015); Putnam, supra note 3, at 174–83; Jennifer King Rice, Investing in Equal Opportunity: What Would It Take to Build the Balance Wheel? 8–9 (2015); Darling-Hammond, supra note 3, at 84–91; Orfield, supra note 3, at 53, 67–71; Phillips & Chin, supra note 3, at 490–99. Further, they are more likely to learn in segregated environments that have lower levels of peer group competition and support. 6See Linda Darling-Hammond, The Flat World and Education: How America’s Commitment to Equity Will Determine Our Future 37 (2010); Darling-Hammond, supra note 3, at 83; Nance, Persisting Inequalities, supra note 3; see also Gary Orfield et al., Brown at 60: Great Progress, a Long Retreat and an Uncertain Future 5 (2014) (documenting the problem of “double segregation,” meaning that a large and growing percentage of schools serve high concentrations of students who are both poor and students of color); Halley Potter, Kimberly Quick & Elizabeth Davies, A New Wave of School Integration: Districts and Charters Pursuing Socioeconomic Diversity 3 (2016) (observing that more than one-third of African-American students and Hispanic students attend schools where more than 90% of the students are students of color).

Another racial inequality that has received much less attention, but still deserves our consideration, is the disparate use of strict security measures in schools serving primarily students of color. Strict security measures, for purposes of this Article, include practices such as relying on law enforcement officers to monitor and discipline students; using metal detectors (either hand-held or walk-through); performing random searches of students’ personal belongings, lockers, or persons; controlling access to school campuses by locking or monitoring gates; and using surveillance cameras. All school officials monitor (and should monitor) their students to some degree. Indeed, among school officials’ most important responsibilities are keeping students safe and promoting an orderly climate conducive to learning. However, there comes a point where monitoring students no longer enhances the learning environment, but impedes it, especially when school officials rely on a combination of the strict security measures listed above, which can create an intense surveillance environment.

In fact, for many students, particularly students attending schools where the majority of students are students of color, school too often resembles a prison. For example, Minerva Dickson, a New York high school student, recently described her everyday school experience in this fashion. 7See Daryl Khan, Perps or Pupils? Safety Policy Creates Prison-like New York City Schools, Juv. Just. Info. Exchange (Sept. 20, 2012), http://jjie.org/york-story/93676/. Every morning when Minerva arrived at school, she waited in a long line to swipe her identification card through a machine. 8Id. If the machine recognized the card, it beeped and flashed a green light. 9Id. If it did not, the machine made a loud buzzing sound and flashed a red light. 10Id. Once she cleared the machine, she was funneled toward fully-uniformed police officers who had handcuffs dangling from their belts. 11Id. Each day, while the school safety agents watched her, she had to remove her shoes, jewelry, and hairpins. 12Id. She would place her purse and backpack on a conveyer belt and wait for an agent to signal her to come forward. 13Id. She then would spread her arms and legs as another agent ran a metal detector wand around her frame. 14Id. Finally, she would be permitted to put on her shoes, collect her belongings, and hurry to her first class. 15Id. When asked how she felt about this security process, she replied, “[t]hey treated us like criminals. It made me hate school. When you cage up students like that it doesn’t make us safe, it makes things worse.” 16Id.

Another example of this phenomenon comes from Edward Ward, an honor roll student at DePaul University, who also described the conditions of his high school as prison-like in his testimony to a U.S. Senate Committee. 17Ending the School-to-Prison Pipeline: Hearing Before the S. Comm. on the Judiciary Subcomm. on the Const., Civil Rights and Human Rights of the S. Comm. on the Judiciary, 112th Cong. 1 (2012) (testimony of Edward Ward, Blocks Together, Dignity in Schools Campaign), https://www.judiciary.senate.gov/imo/media/doc/12-12-12WardTestimony.pdf. Edward attended high school on the west side of Chicago, where 90% of the students were low-income, and all of the students were students of color. 18Id. at 1–2. He stated that

[f]rom the moment we stepped through the doors in the morning, we were faced with metal detectors, x-ray machines and uniformed security. Upon entering the school, it was like we stepped into a prison. . . . [T]he halls were full with school security officers whose only purpose seemed to be to serve students with detentions or suspensions. 19Id. at 1, 3.

Because of this tense surveillance environment and the way the school police officers treated him and his fellow students, he testified that he “could slowly see the determination to get an education fade from the faces of [his] peers because they were convinced that they no longer mattered . . . . [T]he last thing that would work is to place them in institutions of confinement and control.” 20Id. at 3–4.

Similarly, at a New Orleans high school, students each morning passed through metal detectors monitored by a police officer and several security guards. 21See Ellen Tuzzolo & Damon T. Hewitt, Rebuilding Inequity: The Re-Emergence of the School-to-Prison Pipeline in New Orleans, 90 High Sch. J., Dec. 2006–Jan. 2007, at 59, 66. Those guards scanned each student individually with a hand-held metal detector and rummaged through students’ personal bags. 22Id. If guards discovered cell phones, oversized jewelry, or belts with certain buckles, then they confiscated them. 23Id. Students who triggered the metal detector three times after the police could not find any metal items were sometimes sent home. 24Id. On certain days, students who were not in a classroom by 9:00 a.m. were locked out of the classrooms, swept into an auditorium by an army of guards monitoring the hallway, and then suspended. 25Id.

An intense surveillance environment can exist in any kind of school. 26See Aaron Kupchik, Things are Tough All Over: Race, Ethnicity, Class and School Discipline, 11 Punishment & Soc’y 291, 302–03 (2009) (observing that surveillance and policing in schools is pervasive, although more intense measures are more common in schools that serve poorer and minority students). However, empirical research shows that schools where the majority of students are students of color are more likely to rely on various combinations of strict security measures than schools serving primarily white students. 27See infra Part III. Critically, these racial disparities remain even after taking into account other factors that might explain why schools decide to rely on strict security measures, such as neighborhood crime, school crime, and school disorder. 28See infra Part III. This weakens the argument that such disparities exist solely because of safety concerns. 29See infra Part III. Instead, these studies support the conclusion that other factors, such as implicit racial bias, may to some degree influence school officials’ decisions to implement these measures. 30See infra Part IV. Of course, more empirical studies, including more observational studies, are needed to investigate the effect of implicit bias on school administrators’ decisionmaking generally, including their decisions to implement strict security measures. In fact, implicit racial bias may explain how some school officials can act in good faith (i.e., by not making decisions based on overt discriminatory intent), but still unconsciously perpetuate racial inequalities in the public school system by making adverse decisions based on unconscious stereotypes and attitudes toward students of color. 31See Sarah Redfield, Can New Thinking Help Reverse the School-to-Prison Pipeline?, 5 A.B.A. Diversity Voice, Summer 2014, at 4, 4, http://www.americanbar.org/content/dam/aba/administrative/diversity/DVsummer2014.authcheckdam.pdf.

The problems associated with intense surveillance environments are at least two-fold. First, research suggests that the use of strict security measures to maintain order and control may contribute to the formation of poor and dysfunctional learning climates, which means that students of color may often have learning opportunities inferior to those of other students. 32See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 82–83. While poor school climates and dysfunctional learning environments often result from several factors, research suggests that over-reliance on intense surveillance methods and punitive methods generally hinders a school from developing a healthy school climate that is conducive to learning. See infra Part II. Second, strict security measures often are a component of a larger, more complex phenomenon frequently referred to as the “school-to-prison pipeline.” 33See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83. The school-to-prison pipeline is a metaphor used to describe the intersection of the K–12 public school system and the criminal justice system. 34See Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 923. It denotes the practice of referring students to law enforcement for committing certain offenses while at school or creating conditions whereby students are more likely to become involved with law enforcement and the juvenile justice system, such as by suspending or expelling them. 35See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83; see also Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 923–24. Many school officials rely on strict security measures in conjunction with other punitive discipline policies, such as zero-tolerance policies, to maintain order in their schools. 36See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83. “Zero-tolerance policies” are policies that mandate “the application of predetermined consequences, most often severe and punitive in nature, that are intended to be applied regardless of the gravity of behavior, mitigating circumstances, or situational context.” Am. Psychological Ass’n Zero Tolerance Task Force, Are Zero Tolerance Policies Effective in the Schools? An Evidentiary Review and Recommendations, 63 Am. Psychologist 852, 852 (2008). For a more complete discussion on zero-tolerance policies, see Derek W. Black, The Constitutional Limit of Zero Tolerance in Schools, 99 Minn. L. Rev. 823 (2015); and Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 932–34. Such practices end up pushing more students out of school, especially low-performing students, 37See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94, 99–102; see also Barry C. Feld, T.L.O and Redding’s Unanswered (Misanswered) Fourth Amendment Questions: Few Rights and Fewer Remedies, 80 Miss. L.J. 847, 884–95 (2011) (explaining how the combination of certain policies and practices such as strict security measures and zero-tolerance policies have contributed to the school-to-prison pipeline); Jeremy D. Finn & Timothy J. Servoss, Security Measures and Discipline in American High Schools, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 44, 45 (Daniel J. Losen ed., 2015) (finding empirically that strict security measures are positively associated with increased suspension rates). thereby increasing the likelihood that those students will become involved in the justice system. 38See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 319–24. Empirical research shows that these harsh policies disproportionately affect minority students, contributing to the vast inequalities that exist in our education and justice systems. 39See Finn & Servoss, supra note 37, at 55 (empirically demonstrating that more African-American students are suspended in high security schools than white students).

This Article contributes to the literature on education law, education policy, and racial inequalities in the following manner. First, it presents empirical data on school surveillance practices, including an original analysis of restricted data recently released by the U.S. Department of Education after the shootings at Sandy Hook Elementary School, which show that schools serving primarily students of color are more likely to rely on intense surveillance methods than other schools. These data also reveal that racial disparities remain even after controlling for other factors that might explain the use of strict security measures, such as neighborhood crime and the levels of crime and disorder that exist in schools, weakening the theory that legitimate safety concerns justify these observed racial disparities. Second, this Article discusses the role that implicit racial bias may have in school officials’ decisions to implement intense surveillance methods. Third, this Article proposes legislation and strategies that federal lawmakers, state lawmakers, and school officials should adopt to counteract the effect of implicit racial bias on school officials’ decisions to implement strict security measures (and other decisions they make). Implementing these recommendations will help create better learning climates that benefit students of all races.

This Article proceeds in five parts. Part I describes recent movements in the law and other phenomena that have motivated school officials to increasingly rely on strict security measures to monitor students and maintain order in their schools. Part II discusses the educational and sociological harms to students that result from overusing strict security measures in schools, particularly when school officials choose to adopt those measures based on illegitimate criteria such as race (either consciously or unconsciously). Part III presents the results of several empirical analyses revealing the disparate use of strict security measures along racial lines, including an original empirical analysis of recent, restricted data that the U.S. Department of Education released after the Sandy Hook shootings. Part IV discusses the concept of implicit racial bias and its possible influence on school officials’ decisions to rely on intense surveillance methods. Finally, Part V proposes legislation and strategies for federal lawmakers, state lawmakers, and school officials to adopt to address the negative trends the empirical analyses reveal, including measures to counteract the effect of implicit racial bias on school officials’ decisions to implement strict security measures.

I. The Movement Toward Increased Reliance on Strict Security Measures in Schools

The reasons why more schools, particularly those serving primarily minority students, have increasingly relied on strict security measures are complex and varied. 40See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 91–102; Nance, Students, Security, and Race, supra note 1, at 7–16. This section describes the recent movements in the law and other developments that have occurred over the last few decades, which have motivated many school officials to intensify their surveillance of students.

A. The U.S. Supreme Court Has Weakened Students’ Fourth Amendment Rights

An important driving force behind the proliferation of intense surveillance measures in schools is the decline of students’ Fourth Amendment rights. Over the last three decades, the U.S. Supreme Court has rendered a series of decisions that provided school officials with more discretion to promote orderly, safe environments. 41See Nance, Random, Suspicionless Searches, supra note 1, at 376–87; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 91–102; Nance, Students, Security, and Race, supra note 1, at 7–16; James E. Ryan, The Supreme Court and Public Schools, 86 Va. L. Rev. 1335, 1415 (2000) (“[T]he Court’s decisions regarding student searches rest on the value-laden view that maintaining discipline is necessary to preserve the educational process of schools.”). Nevertheless, it is important to recognize that New Jersey v. T.L.O. held that students do retain their Fourth Amendment rights while at school. 469 U.S. 325, 332–33 (1985). Prior to T.L.O., some lower courts applied the in loco parentis doctrine, holding that students did not possess Fourth Amendment rights while at school because school administrators acted in the place of parents during school hours. See Nance, Random, Suspicionless Searches, supra note 1, at 377 n.38; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 128 n.284. For example, the Court has held that school officials do not need to obtain a search warrant, meet the probable cause standard, or have an individualized suspicion that a student is involved in illicit activity before conducting a search. 42See T.L.O., 469 U.S. at 340–42; Vernonia Sch. Dist. 47J v. Acton, 515 U.S. 646, 653–54 (1995); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 95–96; Nance, Students, Security, and Race, supra note 1, at 7–8. These decisions allow school officials to legally create intense surveillance environments in their schools, even when those environments may not serve students’ best interests. 43See Nance, Students, Security, and Race, supra note 1, at 8; see also infra Part II. They also allow school officials to provide evidence of wrongdoing to prosecutors that they obtained from searching students under circumstances that might have rendered that evidence inadmissible if it had been obtained under similar circumstances outside of the school context. 44See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 328–29; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 936–40; see also Catherine Y. Kim, Daniel J. Losen & Damon T. Hewitt, The School-to-Prison Pipeline: Structuring Legal Reform 118–20 (2010); Catherine Y. Kim, Policing School Discipline, 77 Brook. L. Rev. 861, 865–67 (2012).

The Court addressed students’ Fourth Amendment rights in schools for the first time in New Jersey v. T.L.O. 45469 U.S. 325 (1985). There, after a teacher spotted two students smoking in the bathroom, the teacher promptly escorted the students to the principal’s office. 46Id. at 328. T.L.O.’s companion admitted to a school official that she had been smoking, but T.L.O. denied the allegations. 47Id. The school official demanded that T.L.O. hand over her purse and, after T.L.O. complied, the school official searched through its contents and uncovered a package of cigarettes, a small amount of marijuana, a substantial amount of one-dollar bills, a list of students who owed money to T.L.O, and two letters indicating that T.L.O. was dealing marijuana. 48Id. When the state of New Jersey brought juvenile delinquency charges against T.L.O., T.L.O. moved to suppress the evidence the school official obtained, claiming that it resulted from an unlawful search. 49Id. at 329.

After determining that students do indeed hold Fourth Amendment rights in schools, 50Id. at 333–37. the Court concluded that the school official’s search complied with the Fourth Amendment. 51Id. at 332–33. Although the Court acknowledged students’ legitimate expectations of privacy in the belongings they bring to school and in their persons, 52Id. at 337–38. the Court held that these expectations of privacy must be balanced against educators’ need to maintain order and discipline so that learning can take place. 53Id. at 339–40. Accordingly, it held that school officials do not need to obtain a warrant before searching a student. 54Id. at 340. It further held that a school official’s suspicion of student wrongdoing does not need to rise to the level of probable cause before she can conduct a search. 55Id. at 340–41. Rather, a court should determine whether a search was reasonable under the circumstances by applying a twofold inquiry: (1) “whether the . . . action was justified at its inception,” and (2) whether the search “was reasonably related in scope to the circumstances which justified the interference in the first place.” 56Id. at 341(quoting Terry v. Ohio, 392 U.S. 1, 20 (1968)).

The next U.S. Supreme Court decision to evaluate students’ Fourth Amendment rights, Vernonia School District 47J v. Acton, 57515 U.S. 646 (1995). involved a very different set of circumstances. Rather than examining the constitutionality of a search performed on an individual student, it examined the legality of a school district’s random, suspicionless drug-testing program on students participating in interscholastic sports programs. 58Id. at 650. In a 6–3 decision authored by Justice Scalia, the Court declined to hold that individualized suspicion was essential to conduct a lawful search. 59Id. at 653. Rather, the Court held that it must balance a search’s “intrusion on the individual’s Fourth Amendment interests against its promotion of legitimate governmental interests.” 60Id. at 652–53 (quoting Skinner v. Ry. Labor Execs.’ Assoc., 489 U.S. 602, 619 (1989)). It then set forth the following three factors for courts to consider: (1) “the scope of the legitimate expectation of privacy at issue;” (2) “the character of the intrusion that is complained of;” and (3) “the nature and immediacy of the governmental concern at issue . . . and the efficacy of this means for meeting it.” 61Id. at 658–60.

Evaluating each of these factors, the Court first acknowledged that students retain an expectation of privacy while at school, but explained that the scope of their Fourth Amendment rights is “different in public schools than elsewhere” because of “the schools’ custodial and tutelary responsibility for children.” 62Id. at 656. The Court reasoned that because students must submit to various physical examinations, including vision, hearing, dental, dermatological, and scoliosis screenings, their expectations of privacy in schools, “[p]articularly with regard to medical examinations and procedures,” are reduced. 63Id. at 656–57. Moreover, the Court held that student athletes have even lower expectations of privacy because those students choose to participate in athletic programs, and they encounter certain conditions, such as locker rooms, where youth have less privacy. Id.

Second, the Court concluded that the drug tests were minimally intrusive because drug testing resembled conditions that students often face when using public restrooms; their purpose was limited only to ascertain whether a student athlete was using drugs, and the test results were disclosed only to a limited number of school officials, not law enforcement officers. 64Id. at 658.

Third, the Court concluded that the school district’s interest in deterring student drug use, especially among student athletes, was important in light of drugs’ physical, psychological, and addictive effects. 65Id. at 661. The Court also concluded that the school district’s concern was immediate because “a large segment of the student body, particularly those involved in interscholastic athletics, was in a state of rebellion . . . [which] was being fueled by alcohol and drug abuse.” 66Id. at 662–63. Thus, according to the Court, these considerations outweighed any privacy rights those students possessed. 67Id. at 664–65.

Seven years later, in Board of Education v. Earls, the Court again evaluated the constitutionality of a random, suspicionless drug-testing program. 68536 U.S. 822 (2002). This drug-testing program, however, was broader than the drug-testing program in Acton because it involved all students participating in extracurricular activities, not just those involved in student athletics. 69Id. at 825. The United States Court of Appeals for the Tenth Circuit determined that the school district’s random, suspicionless drug-testing policy violated the Fourth Amendment because the school district had not demonstrated that there was an identifiable drug abuse problem among students who participated in extracurricular activities. 70Earls ex rel. Earls v. Bd. of Educ. of Tecumseh Pub. Sch. Dist., 242 F.3d 1264, 1278 (10th Cir. 2001). The U.S. Supreme Court, however, reversed the Tenth Circuit in a 5–4 decision authored by Justice Thomas. 71Earls, 536 U.S. at 824–25. The Court evaluated the three factors outlined in Acton and largely reached the same conclusions. 72Id. at 830–38. However, the Court’s holding was broader than its holding in Acton. While the Court noted that the school district “presented specific evidence of drug use,” 73Id. at 834. it also held that it was unnecessary for the school district to provide evidence of a drug abuse problem before imposing a suspicionless drug-testing policy. 74Id. at 835. The Court reasoned that the school district’s program complied with Fourth Amendment jurisprudence because “the nationwide drug epidemic makes the war against drugs a pressing concern in every school.” 75Id. at 834. Even more recently, the Court evaluated students’ Fourth Amendment rights again in Safford Unified School District Number 1 v. Redding, 557 U.S. 364 (2009). There, the Court decided whether school officials violated the Fourth Amendment when they performed a strip search on a thirteen-year-old female student attempting to find unauthorized prescription and over-the-counter drugs. Id. at 368. Relying on the two-factor test in T.L.O., the Court concluded that the strip search violated the Fourth Amendment because the scope of the intrusion was excessive in light of the student’s age and the nature of the alleged wrongdoing. Id. at 378. However, the Court also held that the qualified immunity doctrine protected the school officials from liability, illustrating how difficult it is for students to obtain monetary relief for illegal searches. Id. at 378–79; see also Feld, supra note 37, at 947–54; Nance, Students, Security, and Race, supra note 1, at 12 n.64.

These decisions have paved the way for school officials to legally employ an array of search practices, including performing random, suspicionless searches of students in forms other than random drug tests. 76Nance, School Surveillance and the Fourth Amendment, supra note 1, at 95–96, 121; Nance, Students, Security, and Race, supra note 1, at 12–13. Nevertheless, as I have argued elsewhere, these decisions should not justify random, suspicionless searches that are highly intrusive, such as searches through students’ belongings, unless school officials have particularized evidence of a drug or weapons problem in their school. See Nance, Random, Suspicionless Searches, supra note 1, at 391–94. For example, based on this precedent, lower courts have justified the use of metal detectors in schools 77See, e.g., Hough v. Shakopee Pub. Sch., 608 F. Supp. 2d 1087, 1104 (D. Minn. 2009); In re Latasha W., 70 Cal. Rptr. 2d 886 (Cal. Ct. App. 1998); State v. J.A., 679 So. 2d 316, 319–20 (Fla. Dist. Ct. App. 1996); In re F.B., 726 A.2d 361, 366 (Pa. 1999). and searching through students’ lockers. 78See, e.g., State v. Jones, 666 N.W.2d 142, 150 (Iowa 2003); In re Patrick Y., 746 A.2d 405, 414–15 (Md. 2000); In re Isiah B., 500 N.W.2d 637, 641 (Wis. 1993). Nevertheless, courts disagree regarding whether students retain an expectation of privacy in their lockers. See Kim, Losen & Hewitt, supra note 44, at 116; Feld, supra note 37, at 933–37; Nance, Random, Suspicionless Searches, supra note 1, at 411–12. Further, arguably, students should not lose their expectation of privacy in their personal belongings only because they place those belongings in a locker. See Nance, Random, Suspicionless Searches, supra note 1, at 411–12; Nance, Students, Security, and Race, supra note 1, at 13 n.66. Moreover, there are no protections against school officials’ use of a combination of security measures, such as relying on metal detectors, random sweeps, locked gates, law enforcement officers, and surveillance cameras together, even when their combined use may create an intense, prison-like environment that arguably is highly-intrusive, undignified, and inconsistent with a healthy learning atmosphere. 79Nance, School Surveillance and the Fourth Amendment, supra note 1, at 96; Nance, Students, Security, and Race, supra note 1, at 13.

B. A Response to High-Profile Acts of School Violence

Other powerful forces behind the proliferation of intense surveillance measures in schools include fears and insecurities that violence may erupt in the absence of these measures. 80Aaron Kupchik, Homeroom Security: School Discipline in an Age of Fear 3 (2010) (observing that fears and insecurities motivate schools to adopt security measures in light of highly publicized acts of school violence); Kevin P. Brady, Sharon Balmer & Deinya Phenix, School-Police Partnership Effectiveness in Urban Schools, 39 Educ. & Urb. Soc’y 455, 456 (2007) (“An increasing fear of school violence coupled with the public’s misperceptions of the actual degree of violence in our nation’s schools has caused school officials, especially those located in urban areas, to implement more punitive-based school discipline policies and practices for responding to and preventing student crime and violence.”); Paul Hirschfield, School Surveillance in America: Disparate and Unequal, in Schools Under Surveillance: Cultures of Control in Public Education 38, 38 (Torin Monahan & Rodolfo D. Torres eds., 2010) (“The importation of surveillance tactics from criminal justice and the military into schools is most commonly attributed to elevated fears of school violence and a growing realization that ‘it can happen here.’”); Thomas Mowen, John Brent & Aaron Kupchik, School Crime and Safety, in The Handbook of Measurement Issues in Criminology and Criminal Justice 434, 440 (Beth M. Huebner & Timothy S. Bynum eds., 2016) (“[W]idespread insecurity coupled with anxieties about youth vulnerability have forced schools to take a proactive stance toward preventing the occurrence of violent acts on their premises.”); Nance, School Surveillance and the Fourth Amendment, supra note 1, at 92–94. Despite the fact that schools generally are safe and remain among the safest places for children to reside, 81See Kim Brooks, Vincent Schiraldi & Jason Ziedenberg, School House Hype: Two Years Later 5 (2000) (reporting that there “is a less than one in a million chance of a school aged youth dying or committing suicide on school grounds”); Barbara Fedders, Jason Langberg & Jennifer Story, School Safety in North Carolina: Realities, Recommendations & Resources 4 (2013) (“School violence that results in death is extremely rare. Young people are much more likely to be harmed in the home or on the street than they are in schools.” (footnote omitted)); Randall R. Beger, The “Worst of Both Worlds”: School Security and the Disappearing Fourth Amendment Rights of Students, 28 Crim. Just. Rev. 336, 338 (2003) (“Contrary to popular belief, schools remain among the safest places for children.”); Randy Borum et al., What Can Be Done About School Shootings? A Review of the Evidence, 39 Educ. Researcher 27, 27 (2010) (reporting that the number of homicides that occur on school grounds represents less than one percent of the annual homicides of children ages five to eighteen, and that “any given school can expect to experience a student homicide about once every 6,000 years”); Pedro A. Noguera, Schools, Prisons, and Social Implications of Punishment: Rethinking Disciplinary Practices, 42 Theory into Prac. 341, 343 (2003) (“Despite surveys that suggest a growing number of teachers and students fear violence in school, schools in the United States are generally safe places.” (citation omitted)); Arne Duncan, Resources for Schools to Prepare for and Recover from Crisis, Dep’t of Educ.: Homeroom (Dec. 17, 2012), http://www.ed.gov/blog/2012/12/resources-for-schools-to-prepare-for-and-recover-from-crisis/ (“Schools are among the safest places for children and adolescents in our country, and, in fact, crime in schools has been trending downward for more than a decade.”). widely publicized events of school violence often distort the public’s perception of school safety and create “moral panic,” 82Beger, supra note 81, at 338 (“Widely publicized incidents of juvenile violence in public schools have created the public misconception that such behavior is commonplace.”); Borum et al., supra note 81, at 27 (“[S]chool shootings receive such intense publicity, and are such inherently disturbing events, that they generate an inflated perception of danger.”); Matthew J. Mayer & Peter E. Leone, School Violence and Disruption Revisited: Equity and Safety in the School House, 40 Focus on Exceptional Child., Sept. 2007, at 1, 6 (“[M]edia coverage of school violence has shaped the public’s beliefs, and in many cases has led to a distorted perception of violence in schools, as well as adolescent violence more generally.”); Elizabeth S. Scott, Miller v. Alabama and the (Past and) Future of Juvenile Crime Regulation, 31 L. & Inequality 535, 541 (2013) (explaining that although serious acts of school violence are rare, after the shootings at Columbine High School, “legislatures across the country rushed to pass strict zero tolerance laws, making it a crime to threaten violence in school”); cf. Donna M. Bishop & Barry C. Feld, Juvenile Justice in the Get Tough Era, in Encyclopedia of Criminology and Criminal Justice 2766, 2768 (Gerben Bruinsma & Davis Weisburd eds., 2014) (explaining that violent incidents caused by juveniles attract a significant amount of media attention that can result in a “moral panic,” where “the media, politicians, and the public reinforce each other in an escalating alarmist response that exaggerates the magnitude of the threat and produces urgent calls to ‘do something’”). putting pressure on school officials to demonstrate to parents and community members that they are taking concrete measures to prevent school violence. 83Nance, School Surveillance and the Fourth Amendment, supra note 1, at 92–93. For example, some schools responded to the shootings at Columbine High School in April 1999 by increasing police presence and installing metal detectors, surveillance cameras, and buzz-in doors. 84See Jaana Juvonen, School Violence: Prevalence, Fears, and Prevention, RAND (2001), http://www.rand.org/pubs/issue_papers/IP219/index2.html; see also Rich Henson, Safety On ‘Everybody’s Mind’ For Schools’ Reopening Reacting to the Carnage at Columbine, Districts Across the Region Are Stepping Up Measures to Allow a Secure and Peaceful School Year, Philly.com (Aug. 29, 1999), http://articles.philly.com/1999-08-29/news/25483433_1_administrators-school-safety-summit-safety-measures; John D. Sutter, Columbine Massacre Changed School Security, CNN (Apr. 20, 2009, 9:30 AM), http://www.cnn.com/2009/LIVING/04/20/columbine.school.safety/index.html?iref=24hours. Similarly, after the shootings at Sandy Hook Elementary School in December 2012, many schools again responded by implementing a range of security measures in an effort to keep children safe. 85See Maggie Clark, Back to School: Security Heightened After Newtown, USA Today (Aug. 14, 2013, 4:00 PM), http://www.usatoday.com/story/news/nation/2013/08/12/stateline-school-security/2642155/; Mary Beth Marklein, Schools Tighten Security After Sandy Hook, USA Today (Sept. 24, 2013 12:12 PM), http://www.usatoday.com/story/news/nation/2013/09/23/schools-step-up-security-measures-in-wake-of-sandy-hook/2844423/. Indeed, the threat that a serious violent incident may occur or that a school may become the next Columbine can lead school officials, parents, and others to conclude that adopting strict security measures is worth any trade-off if such measures will keep students safe. 86Torin Monahan & Rodolfo D. Torres, Introduction, in Schools Under Surveillance: Cultures of Control in Public Education 1, 2–3 (Torin Monahan & Rodolfo D. Torres eds., 2010).

C. The Movement Towards Criminalizing School Discipline

Relatedly, another driving force behind the proliferation of intense surveillance measures in schools is a general movement by lawmakers and school officials to criminalize school discipline. 87For a broader discussion on the criminalization of school discipline, see Henry A. Giroux, Racial Injustice and Disposable Youth in the Age of Zero Tolerance, 16 Int’l J. Qualitative Stud. Educ. 553 (2003); Paul J. Hirschfield, Preparing for Prison? The Criminalization of School Discipline in the USA, 12 Theoretical Criminology 79 (2008); Kathleen Nolan & Jean Anyon, Learning to Do Time: Willis’s Model of Cultural Reproduction in an Era of Postindustrialism, Globalization, and Mass Incarceration, in Learning to Labor in New Times 133 (Nadine Dolby, Greg Dimitriadis & Paul Willis eds., 2004); Matthew T. Theriot, School Resource Officers and the Criminalization of Student Behavior, 37 J. Crim. Just. 280 (2009); and Kerrin C. Wolf, Arrest Decision Making by School Resource Officers, 12 Youth Violence & Juv. Just. 137 (2014). Although this complex movement will not be fully discussed here, it parallels the general “tough on crime” movement that has occurred in our nation over roughly the same time period. 88See Kathleen Nolan, Police in the Hallways: Discipline in an Urban High School 22–24 (2011); Bishop & Feld, supra note 82, at 2770; Giroux, supra note 87, at 557–58; Mowen, Brent & Kupchik, supra note 80, at 435. In response to rising juvenile crime rates from the mid-1980s to the mid-1990s and high-profile acts of school violence committed by students, lawmakers, and school officials throughout the nation created laws and policies designed to remove students from school or refer them to law enforcement for committing certain offenses. 89Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 328; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 930–32. The widespread adoption of zero-tolerance policies is a good illustration of this movement. See Scott, supra note 82.

In connection with this movement (and in an effort to prevent school violence generally), the federal government and state governments also passed several laws aimed at intensifying student surveillance by providing schools with money to purchase security equipment and hire law enforcement officers. 90Nance, School Surveillance and the Fourth Amendment, supra note 1, at 97–99; Nance, Students, Security, and Race, supra note 1, at 13–15; see also Juvonen, supra, note 84. For example, through the U.S. Department of Justice’s Community Oriented Policing Services (COPS) program, the federal government has funneled hundreds of millions of dollars to schools for security equipment and law enforcement officers. 91See Office of Cmty. Oriented Policing Servs., DOJ, Fact Sheet: Secure Our Schools (2011), http://www.cops.usdoj.gov/pdf/2011AwardDocs/CSPP-SOS-CHP/2011-SOS-Post-FactSheet.pdf. Likewise, several state governments have enacted laws that have provided schools with money for strict security measures. 92See, e.g., Ind. Code § 10-21-1-2 (2016) (“The Indiana secured school fund is established to provide matching grants [with which a school may] (1) employ a school resource officer . . . (3) purchase equipment and technology to: (A) restrict access to school property; or (B) expedite notification of first responders.”); Miss. Code Ann. § 37-3-83 (2016) (“[T]he School Safety Grant Program shall offer . . . (a) [m]etal detectors; (b) [v]ideo surveillance cameras, communications equipment and monitoring equipment for classrooms, school buildings, school grounds and school buses . . . .”); N.Y. Educ. Law § 2814 (McKinney 2016) (“Programs eligible for funding pursuant to this section may include, but not limited to: . . . (iv) metal detectors, intercom and other intra-school communication devices and other devices . . . .”); 24 Pa. Stat. and Cons. Stat. Ann. § 13-1302-A (West 2016) (authorizing “targeted grants to school entities to fund programs which address school violence, including: . . . (9) Security planning, purchase of security-related technology which may include metal detectors, protective lighting, surveillance equipment . . . deadbolts and theft control devices and training”); Schools Against Violence in Education (SAVE) Act, Tenn. Code Ann. § 49-6-805, 811 (2016) (authorizing the Tennessee School Safety Center to provide grants to ensure that schools implement “[p]olicies and procedures relating to school building security, including . . . the use of school resource officers, security devices or security procedures”); Public School Security Equipment Grant Act of 2013, Va. Code Ann. § 22.1-280.2:2 (2016) (providing competitive grants to schools for security equipment).

D. High-Stakes Testing Laws

High-stakes testing laws also may have the unintended consequence of motivating some school officials to increase their surveillance of students as part of an effort to exclude low-performing students from their schools. 93See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94–95; Nance, Students, Security, and Race, supra note 1, at 15–16. High-stakes testing laws compel schools to test students at certain stages in core subject areas, such as mathematics and reading, and may carry adverse consequences for schools whose students fail to meet certain standards. 94See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94–95; Nance, Students, Security, and Race, supra note 1, at 15–16. For example, the Every Student Succeeds Act, Pub. L. No. 114-95, 129 Stat. 1802 (2015), which is the latest reauthorization of the Elementary and Secondary Education Act and recently replaced the No Child Left Behind Act, requires states receiving federal education funds to formulate and conduct student academic assessments. See id. § 1005 (amending 20 U.S.C. § 6311(b)(2) (2012)). However, one of the hallmarks of the Every Student Succeeds Act is that it precludes the federal government from determining how to weigh those academic assessments for accountability purposes, leaving that decision to the states. See id. § 1005 (amending 20 U.S.C. § 6311(e)(1)(B)(iii)); see also Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 940 n.103. Many scholars fear that high-stakes testing laws may motivate some school officials to exclude low-performing students to avoid having their low scores against them. 95See Kupchik, supra note 80, at 28; The Advancement Project, Test, Punish, and Push Out: How “Zero Tolerance” and High-Stakes Testing Funnel Youth into the School-to-Prison Pipeline 28–33 (2010); Linda Darling-Hammond, Race, Inequality, and Educational Accountability: The Irony of ‘No Child Left Behind’, 10 Race Ethnicity & Educ. 245, 252–55 (2007); Deborah Gordon Klehr, Addressing the Unintended Consequences of No Child Left Behind and Zero Tolerance: Better Strategies for Safe Schools and Successful Students, 16 Geo. J. on Poverty L. & Pol’y 585, 602–03 (2009); Rachel F. Moran, Sorting and Reforming: High-Stakes Testing in the Public Schools, 34 Akron L. Rev. 107, 115 (2000); James E. Ryan, The Perverse Incentives of The No Child Left Behind Act, 79 N.Y.U. L. Rev. 932, 969–70 (2004). David Figlio examined a large dataset of Florida school district records containing information about student suspensions and test scores. 96David N. Figlio, Testing, Crime and Punishment, 90 J. Pub. Econ. 837, 839 (2006). He compared the punishment of students from incidents involving two students where both students were suspended and where he could observe both students’ test scores from the prior year. 97Id. He found that schools tended to give harsher punishments to low performing students than to high performing students. 98Id. Further, this punishment gap grew significantly wider during the testing window for students in grades that administered high stakes tests. 99Id. He concluded that his results indicated “that schools may be using student discipline as a tool to manipulate aggregate test scores.” 100Id. at 850. For schools that have adopted this exclusionary mindset, school officials may suspend, expel, transfer to an alternative setting, or refer these low-performing students to law enforcement when they discover wrongdoing using intense surveillance methods. 101See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94–95; Nance, Students, Security, and Race, supra note 1, at 15–16.

E. Schools Lack Resources to Appropriately Address Students’ Needs

Furthermore, some school officials may feel compelled to create an intense surveillance environment because they lack the resources and training to properly educate and manage high numbers of students with acute needs. 102See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 99–102. Many schools, particularly schools located in impoverished areas, serve large numbers of students that suffer from trauma, lack adequate health care, face language barriers, live in abusive, neglectful environments, suffer from malnutrition, may move frequently or even be homeless, and have learning disabilities. 103See Orfield & Lee, supra note 3, at 29–30; Diane Ravitch, Reign of Error: The Hoax of the Privatization Movement and the Danger to America’s Public Schools 290–91 (2013); Darling-Hammond, supra note 3, at 83; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 944; see also Noguera, supra note 81, at 342. Further, educators in impoverished areas more often have fewer resources to meet these serious needs. 104See Bruce Baker et al., Is School Funding Fair? A National Report Card 5–6, 5 fig.2 (5th ed. 2016) (finding that in almost half of the states, school districts that serve more affluent students receive, on average, more money per student than school districts that serve primarily impoverished students); Orfield & Lee, supra note 3, at 29–31; Darling-Hammond, supra note 3, at 81–84; Nance, Persisting Inequalities, supra note 3; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 943–44; Kevin G. Welner & Prudence L. Carter, Achievement Gaps Arise from Opportunity Gaps, in Closing the Opportunity Gap: What America Must Do to Give Every Child an Even Chance 1, 6 (Prudence L. Carter & Kevin G. Welner eds., 2013) (“In a fundamentally unequal and unfair system characterized by widespread poverty and segregation, opportunity gaps are exacerbated when children are assigned to schools with substantially fewer resources than those in nearby middle-class communities.”). When educators do not have adequate resources and training to properly teach students and meet their needs, disorder can emerge and potentially lead to safety problems. 105See Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 944–45; Nance, Students, Security, & Race, supra note 1, at 44–46; Noguera, supra note 81, at 342 (observing that it is the severe needs of students and the inability of schools to meet those needs that causes students to be disruptive and sometimes dangerous at school); see also Matthew P. Steinberg, Elaine Allensworth & David W. Johnson, Student and Teacher Safety in Chicago Public Schools: The Roles of Community Context and School Social Organization 46 (2011) (maintaining that low-performing students are less likely to be engaged in school and more likely to be frustrated and misbehave); Matthew P. Steinberg, Elaine Allensworth & David W. Johnson, What Conditions Support Safety in Urban Schools?: The Influence of School Organizational Practices on Student and Teacher Reports of Safety in Chicago, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 118, 125 (Daniel J. Losen ed., 2015) (explaining that low-achieving students are less likely to be engaged and more likely to act out). However, instead of focusing on meeting students’ needs, improving curriculum and instruction to increase student engagement, and employing evidence-based methods to promote safe, orderly environments that do not disrupt the school climate, 106See infra notes 414–15 and accompanying text; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 336–62 (discussing initiatives to promote safe learning climates that do not involve harsh disciplinary measures); Nance, Students, Security, and Race, supra note 1, at 48–55 (discussing alternative measures to reduce school violence). school officials too often turn to extreme forms of discipline and control that include the use of intense surveillance measures. 107See Ravitch, supra note 103, at 295 (observing that schools serving primarily impoverished students are more likely to rely on harsh discipline policies that include strict surveillance methods and exclusionary discipline practices); NAACP Legal Def. & Educ. Fund, Dismantling the School-to-Prison Pipeline 5 (2005); Hirschfield, supra note 87, at 92 (observing that some educators rely on extreme methods of punishment and control because they believe that they “lack the resources to reverse the downward trajectories of the most troublesome students without compromising the quality of teaching and services aimed at more deserving or promising students”); Noguera, supra note 81, at 345 (observing that schools teaching large numbers of struggling students “operate more like prisons than schools,” “are more likely to rely on guards, metal detectors, and surveillance cameras to monitor and control students,” and have a “fixation on control [that] tends to override all other educational objectives and concerns”).

II. Educational and Sociological Harms of Strict Security Measures

Few maintain that relying on strict security measures is ideal, but many will argue that our children’s safety is paramount and overrides any concerns the use of these measures creates. 108Monahan & Torres, supra note 86, at 2–3; Nance, Students, Security, and Race, supra note 1, at 16. No one can credibly argue that creating safe school environments is unimportant. 109See Jason P. Nance, School Security Considerations After Newtown, 65 Stan. L. Rev. Online 103, 106 (2013) [hereinafter Nance, School Security Considerations]; Nance, Students, Security, and Race, supra note 1, at 16–17. But a difficult truth we must all accept is that it is impossible to protect all students at all times and in all places, including while they are at school. 110See Duncan, supra note 81 (explaining that not all tragedies that happen at school can be prevented); see also Nance, Students, Security, and Race, supra note 1, at 16–17. For example, the Columbine shootings occurred in a school with armed guards. Amanda Terkel, Columbine High School Had Armed Guard During Massacre in 1999, Huffington Post (Dec. 23, 2012), http://www.huffingtonpost.com/2012/12/21/columbine-armed-guard_n_2347096.html. Further, as explained above, highly publicized acts of school violence often distort our perceptions of the realities of school safety. 111See supra Part I.B. In contrast to these perceptions, empirical data suggest that schools are among the safest places for children to be. 112For instance, during the 2012–2013 school year, only thirty-one out of the 1186 homicides of five to eighteen-year-old youth occurred at school. Anlan Zhang, Lauren Musu-Gillette & Barbara A. Oudekerk, Indicators of School Crime and Safety: 2015, at iv (2016). Notably, the School-Associated Violent Deaths Study (SAVD), which gathered this information, defines “at school” broadly. Id. at iv n.8. SAVD defined “at school” for purposes of its survey as “on school property, on the way to or from regular sessions at school, and while attending or traveling to or from a school-sponsored event.” Id. Similarly, in 2014, four in one thousand youth between the ages of twelve and eighteen were victims of serious violent crimes at school, but six in one thousand students from the same age group were victims of those crimes outside of school. Id. at 24; see also Nance, Students, Security, and Race, supra note 1, at 17; supra note 81 and accompanying text. Obviously there are some schools that experience serious safety and disciplinary concerns that school officials must address, and perhaps it may be appropriate to implement tighter security measures under certain circumstances. However, over-reliance on punitive, intense surveillance measures to create safe and orderly schools is inconsistent with sound educational and sociological policy and science that suggest otherwise, especially when schools serving primarily minority students are more inclined to apply such measures. 113See infra Part III. Indeed, over-reliance on such measures is particularly troublesome because school officials can better promote safe and orderly learning climates by adopting alternative, softer, evidence-based measures that have proven to be effective in all types of school environments. 114See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 336–62 for a complete discussion of these alternative measures.

A. Over-Reliance on Strict Security Measures May Harm Students’ Interests

Empirical evidence suggests that over-reliance on strict security measures may harm students’ interests in at least two major ways. The first way is that over-reliance on these measures may contribute to poor learning environments that lead to poor student outcomes. 115See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 104–05. Education policy experts understand that trust and cooperation among members of the school community are fundamental to positive learning outcomes, school safety, and healthy learning climates. 116See Roger D. Goddard, Megan Tschannen-Moran & Wayne K. Hoy, A Multilevel Examination of the Distribution and Effects of Teacher Trust in Students and Parents in Urban Elementary Schools, 102 Elementary Sch. J. 3, 3–4 (2001) (observing that trust is an important element of the teaching and learning process); Anne Gregory & Rhona S. Weinstein, The Discipline Gap and African Americans: Defiance or Cooperation in the High School Classroom, 46 J. Sch. Psychol. 455, 458–59 (2008); Megan Tschannen-Moran & Wayne K. Hoy, A Multidisciplinary Analysis of the Nature, Meaning, and Measurement of Trust, 70 Rev. Educ. Res. 547, 547 (2000); see also Nance, Random, Suspicionless Searches, supra note 1, at 395; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 104; Nance, Students, Security, and Race, supra note 1, at 19. Optimal learning conditions for students include experiencing positive relationships with teachers and other students, being treated fairly and with kindness and respect, feeling a sense of belonging in the school community, and having a positive self-image. 117See David Domenici & James Forman Jr., What It Takes to Transform a School Inside a Juvenile Justice Facility: The Story of the Maya Angelou Academy, in Justice for Kids: Keeping Kids Out of the Juvenile Justice System 283, 289 (Nancy E. Dowd ed., 2011) (“High achieving schools are places where a culture of trust dominates.”); Over-Policing in Schools on Students’ Education and Privacy Rights, N.Y. C.L. Union [hereinafter Over-Policing in Schools], http://www.nyclu.org/content/over-policing-schools-students-education-and-privacy-rights (last visited Aug. 1, 2016) (featuring the June 14, 2006, testimony of Donna Lieberman on behalf of the New York Civil Liberties Union before the New York City Council Committee on Education and Public Safety regarding the impact of over-policing in schools on students’ education and privacy rights); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 104. Linda Darling-Hammond maintains that even in challenging environments, schools in which students thrive have “organizational structures that create more coherence and a ‘communal’ orientation, in which staff see themselves as part of a family and work together to create a caring environment.” 118Darling-Hammond, supra note 6, at 65. Indeed, researchers have discovered that teachers who communicate warmly, demonstrate that they care deeply about their students, treat their students fairly, and have high expectations, instill higher levels of trust, positive behavior, and achievement outcomes. 119See Gregory & Weinstein, supra note 116, at 458, 469–70. These findings are by no means limited to children in schools. Tom Tyler has demonstrated that citizens’ evaluations of the courts and police are not primarily related to the outcomes or judgments; rather, they are linked more closely to how fairly they perceive they are being treated. See Tom R. Tyler, Psychological Perspectives on Legitimacy and Legitimation, 57 Ann. Rev. Psychol. 375, 379–80 (2006); Tom R. Tyler, Public Trust and Confidence in Legal Authorities: What Do Majority and Minority Group Members Want from the Law and Legal Institutions?, 19 Behav. Sci. & L. 215, 233 (2001).

However, scholars also maintain that intense surveillance environments in schools disrupt feelings of trust, cooperation, and respect among members of the community by sending a clear signal to students that they are dangerous, violent, and prone to illegal activity. 120See Beger, supra note 81, at 340; Hirschfield, supra note 80, at 46 (maintaining that strict security measures “sour students’ attitudes toward school and school authorities, and undermine a positive, respectful academic environment”); Pedro A. Noguera, Preventing and Producing Violence: A Critical Analysis of Responses to School Violence, 65 Harv. Educ. Rev. 189, 190–91 (1995); Carol Ascher, Gaining Control of Violence in the Schools: A View from the Field, ERIC Digest, Sept. 1994, at 1, 4, http://www.eric.ed.gov/PDFS/ED377256.pdf (observing that strict security measures signal to students that teachers are afraid of their students); see also Nance, Random, Suspicionless Searches, supra note 1, at 395; Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 104; Nance, Students, Security, and Race, supra note 1, at 19–20. Paul Hirschfield observes that strict security measures create social barriers between students, teachers, and school officials, and are “a frequent cause of disunity or discord within the school community.” 121Hirschfield, supra note 80, at 46. Martin Gardner worries that generalized searches convey a message to each student that, “[i]n a very real sense,” each “stands accused [and] has become a ‘suspect,’” which is especially troublesome “given the special relationship of trust which supposedly exists between student and teacher.” 122Martin R. Gardner, Student Privacy in the Wake of T.L.O.: An Appeal for an Individualized Suspicion Requirement for Valid Searches and Seizures in the Schools, 22 Ga. L. Rev. 897, 943 (1988). Gardner continues:

Surely a student even indirectly accused by his teacher as a possible thief or drug user suffers a greater indignity and loss of self-esteem by being subjected to a generalized search than does an airline passenger passing through a metal detector or a driver [through] a checkpoint. Far from “morally neutral,” school searches are instead particularly rife with moral overtones. 123Id. (footnotes omitted). Further, it is important to emphasize that while airline passengers decide voluntarily to board a plane or drive through a checkpoint, students are required to submit to these generalized searches and intense surveillance conditions against their will because of mandatory school attendance laws. See Feld, supra note 37, at 903–04.

Jen Weiss conducted an ethnographic study and found that intense surveillance methods caused students to distrust and avoid school officials. 124Jen Weiss, Scan This: Examining Student Resistance to School Surveillance, in Schools Under Surveillance: Cultures of Control in Public Education 213, 227 (Torin Monahan & Rodolfo D. Torres eds., 2010). Instead of instilling a greater sense of safety, students felt a heightened sense of danger and disillusion. 125Id. at 213–14; see also Brooks, Schiraldi & Ziedenberg, supra note 81, at 3 (“Parents and school boards continue to call for more metal detectors, locker searches and student identification badges, even as students say they feel less safe and report more crime in schools that use these ‘secure’ school procedures.”). Donna Lieberman testified that intense surveillance measures are antithetical to educational environments that promote educational and social growth in youth. 126See Over-Policing in Schools, supra note 117. According to Lieberman, these measures “foster environments where children perceive that they are being treated as criminals; where they are diminished by such perceptions; and where they, consequentially, cultivate negative attitudes toward their schools.” 127Id.; see also Brooks, Schiraldi & Ziedenberg, supra note 81, at 3 (quoting a student saying, “[w]hen I get up to go to school in the morning, I don’t want to feel like I’m going to a correctional facility”); Finn & Servoss, supra note 37, at 44 (maintaining that intense surveillance methods create prison-like conditions that make “students feel defensive and contribute to their emotional and physical disengagement from school”). Timothy Servoss observes that high-security schools require “passivity and compliance” from students to function in their intended manner, but such an environment often leads to conflict because many students are not passive or blindly compliant. 128See Timothy J. Servoss, School Security and Student Misbehavior: A Multi-Level Examination, 46 Youth & Soc.: OnlineFirst 1, 3 (2014). He reasons that if students feel powerless and stifled, they become frustrated and lose motivation to exhibit socially acceptable behavior and adhere to school norms. 129Id.

Importantly, studies examining the social costs incurred in communities whose members are subject to intense surveillance and the threat of government punishment have parallel findings. Tom Tyler, who has studied and written extensively about trust, legitimacy, and voluntary compliance with laws and authority, maintains that intense surveillance environments harm social climates. 130See Tom R. Tyler & Lindsay Rankin, Legal Socialization and Delinquency, in The Oxford Handbook of Juvenile Crime and Juvenile Justice 353, 361 (Barry C. Feld & Donna M. Bishop eds., 2012). According to Tyler, intense surveillance measures imply distrust, which decreases community members’ capacity to feel positively about themselves and their communities as a whole. 131Id.; see also David Kipnis, Trust & Technology, in Trust in Organizations: Frontiers of Theory & Research 39, 46–47 (Roderick M. Kramer & Tom R. Tyler eds., 1996). He maintains that intense surveillance environments cause community members to perceive unjustified intrusions into their privacy as unfair, making them resentful and less willing to obey the law. 132See Tyler & Rankin, supra note 130, at 361; Jason Sunshine & Tom R. Tyler, The Role of Procedural Justice and Legitimacy in Shaping Public Support for Policing, 37 Law & Soc’y Rev. 513, 514 (2003). Accordingly, regardless of whether intense surveillance methods are effective in the short term, they produce unintended social costs—such as distrust, paranoia, and loss of respect for governmental authority—which weaken individuals’ resolve to cooperate with government officials, willingly obey laws, and participate in political processes. 133See Tyler & Rankin, supra note 130, at 361–62.

Apart from contributing to poor learning climates, over-reliance on strict security measures may harm students’ interests in a second significant way. Intense surveillance methods often are a component of a more complex phenomenon frequently referred to as the school-to-prison pipeline. 134See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 102–03. For a definition of the term “school-to-prison pipeline,” see supra text accompanying notes 33–35. Many school officials use intense surveillance methods in conjunction with zero-tolerance policies and other harsh disciplinary measures in their efforts to maintain orderly environments. 135Feld, supra note 37, at 952–53; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83. See supra note 36 for a definition of the term “zero tolerance.” When schools rely on intense surveillance methods and zero-tolerance policies in tandem, school officials automatically suspend, expel, or refer students to law enforcement when they discover students carrying items they are not permitted to bring to school, regardless of the surrounding circumstances, seriousness of the offense, or situational context. 136See Am. Psychol. Ass’n Zero Tolerance Task Force, supra note 36, at 852. This is only one aspect of policies and practices that have combined to put many students, especially students of color, disabled students, and other marginalized groups, on a pathway from school to prison. For an extended discussion of the school-to-prison pipeline and its disproportionate effect on marginalized students, see generally Sarah E. Redfield & Jason P. Nance, American Bar Association Joint Task Force on Reversing the School-to-Prison Pipeline: Preliminary Report (2016); Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 315; and Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1. For example, a recent Minnesota Supreme Court decision, In re Expulsion of A.D., describes a student who was expelled from school for accidentally carrying a three-inch folding pocketknife in her purse. 137In re Expulsion of A.D., 883 N.W.2d 251, 253 (Minn. 2016). During a random, suspicionless search for controlled substances, a school liaison officer searched through A.D.’s locker and found a pocketknife in A.D.’s purse hanging inside. 138Id. at 254. When the school principal and officer confronted A.D., A.D. explained that she had used the pocketknife at her boyfriend’s family farm to cut twine on hay bales the previous weekend and had forgotten to remove it from her purse. 139Id. Even though the school principal believed that A.D. was telling the truth, she expelled A.D. for the remainder of the school year for violating the school district’s weapons policy. 140Id. The Minnesota Supreme Court later overturned A.D.’s expulsion because the school district did not comply with Minnesota Statute § 121A.45, subdiv. 2(a), which provides school districts with authority to suspend or expel students only for a “willful violation of any reasonable school board regulation,” and the Court did not consider A.D.’s act to be a willful violation. Id. at 256, 263–64.

Empirical evidence reveals the association between the use of security measures and student exclusion. Timothy Servoss and Jeremy Finn analyzed school-level data from several national education databases and found that higher levels of security and surveillance in schools were positively associated with higher student suspension rates. 141See Timothy J. Servoss & Jeremy D. Finn, School Security: For Whom and With What Results?, 13 Leadership & Pol’y Schs. 61, 82–83 (2014). Even more troubling, these researchers discovered that school security levels were positively associated with greater disparities in suspension rates among similarly situated African-American and white students. 142See Finn & Servoss, supra note 37, at 53. These suspensions included in-school suspensions and out-of-school suspensions. Id. at 47–48, 53. Specifically, in high-security schools, the odds of an African-American student being suspended from the classroom were 2.7 times higher than for a white student. 143Id. at 53.

These exclusionary practices frequently lead to poor outcomes, including an increased likelihood that more students will become involved in the justice system either immediately or in the future. 144See Feld, supra note 37, at 884–95 (explaining how the combination of school police officers, students’ diminished constitutional rights, school accountability laws, zero-tolerance policies, and strict security measures have put more students on a pathway to prison); Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 321–24; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 956. For example, not only do excluded students miss classroom instruction and possibly fall behind academically, but exclusion also may stigmatize them, promote school avoidance, and preclude access to needed resources. 145See Noguera, supra note 81, at 345–46; Dear Colleague Letter, supra note 4, at 4. Students who eventually return to school but are behind academically can become disengaged and exhibit disruptive behavior because they are frustrated or embarrassed by their inability to meet academic expectations. 146See Steinberg, Allensworth & Johnson, supra note 105, at 46 (explaining that less engaged students are more likely to be frustrated and misbehave); Noguera, supra note 81, at 342, 345–46. In fact, empirical evidence demonstrates that excluding students from school significantly decreases the likelihood that they will graduate from high school. 147See, e.g., Justice Policy Inst., Education Under Arrest: The Case Against Police in Schools 17–18 (2011), http://www.justicepolicy.org/uploads/justicepolicy/documents/educationunderarrest_fullreport.pdf; Robert Balfanz, Vaughn Byrnes & Joanna Hornig Fox, Sent Home and Put Off Track, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 17, 22–29 (Daniel J. Losen ed., 2015) (finding empirically in a longitudinal study of 181,897 Florida students that, after controlling for student demographics and other indicators that a student is not on track to graduate, each suspension decreases the odds that a student will graduate by 20%); Miner P. Marchbanks III et al., The Economic Effects of Exclusionary Discipline on Grade Retention and High School Dropout, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 59, 64 (Daniel J. Losen ed., 2015) (demonstrating empirically that students subject to exclusionary discipline were 23.5% more likely to drop out of school); Tracy L. Shollenberger, Racial Disparities in School Suspension and Subsequent Outcomes: Evidence from the National Longitudinal Survey of Youth, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 31, 36 (Daniel J. Losen ed., 2015) (showing empirically that exclusionary discipline negatively affected graduation rates, but has a magnified impact on African-American males and Latino males); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 321–22. Not graduating from high school leads to many other problems, including unemployment, poverty, and bad health. 148See Clive R. Belfield, Henry M. Levin & Rachel Rosen, The Economic Value of Opportunity Youth 20 (2012), http://www.serve.gov/new-images/council/pdf/econ_value_opportunity_youth.pdf; John M. Bridgeland, John J. Dilulio, Jr. & Karen Burke Morison, The Silent Epidemic: Perspectives of High School Dropouts, at i (2006), https://docs.gatesfoundation.org/documents/thesilentepidemic3-06final.pdf; Nat’l Ctr. Juvenile Justice, Juvenile Offenders and Victims: 2014 National Report 14–15 (Melissa Sickmund & Charles Puzzanchera eds., 2014), http://www.ojjdp.gov/ojstatbb/nr2014/downloads/NR2014.pdf; Andrew Sum et al., The Consequences of Dropping Out of High School: Joblessness and Jailing for High School Dropouts and the High Cost for Taxpayers 7–11 (2009); Bruce Western, Punishment and Inequality in America 17–18 (2006); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 322. Failure to finish high school also is positively related to future involvement in the criminal justice system. 149See Belfield, Levin & Rosen, supra note 148, at 20; Bridgeland, Dilulio & Morison, supra note 148, at i; Sum et al., supra note 148, at 7–11; Western, supra note 148, at 17–18; Thomas Mowen & John Brent, School Discipline as a Turning Point: The Cumulative Effect of Suspension on Arrest, 53 J. Res. Crime & Delinq. 628 (2016) (demonstrating empirically that students who are suspended are more likely to be arrested in later years); Shollenberger, supra note 147, at 36–37 (finding empirically that suspended students are more likely to be involved in the justice system as adults); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 323. Furthermore, excluding a student from school increases the likelihood of immediate involvement in the juvenile justice system. 150Am. Acad. of Pediatrics, Out-of-School Suspension and Expulsion, 112 Pediatrics 1206, 1207 (2003) (observing that when students are not monitored by trained professionals and are at home without parent supervision, they are far more likely to commit crimes); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 323–24. Tony Fabelo and his colleagues conducted a longitudinal study of Texas students and found that when a school suspended or expelled a student for a discretionary offense, the student’s chances of being involved in the juvenile justice system within the subsequent year nearly tripled. 151See Tony Fabelo et al., Breaking Schools’ Rules: A Statewide Study of How School Discipline Relates to Students’ Success and Juvenile Justice Involvement 70 (2011). In addition, they found that with each subsequent exclusionary punishment the student received, the odds of involvement with the juvenile justice system increased even more. 152Id.; see also Alison Evans Cuellar & Sara Markowitz, School Suspension and the School-to-Prison Pipeline, 43 Int’l Rev. L. & Econ. 98, 99 (2015) (finding empirically that students who received out-of-school suspensions were more likely to commit criminal offenses on suspension days than on non-suspension days).

An important corollary to this is that empirical data suggest that over-reliance on exclusionary discipline may not lead to safer school environments in the long run. 153The U.S. Department of Education recently cautioned school districts to “avoid overuse of exclusionary discipline . . . reserv[ing] the use of out-of-school suspensions, expulsions, and alternative placements for the most egregious disciplinary infractions that threaten school safety and when mandated by federal or state law.” U.S. Dep’t of Educ., Guiding Principles: A Resource Guide for Improving School Climate and Discipline 15 (2014) [hereinafter Guiding Principles]. Analyzing data from Chicago Public Schools, Matthew Steinberg and his colleagues found that teachers and students reported lower levels of perceived safety in schools with higher suspension rates, even after accounting for other community and school contextual variables that might explain those perceptions. 154Steinberg et al., supra note 105, at 128–29. They concluded:

[T]his finding suggests that high suspension rates do not sufficiently address the problems that schools face—schools with high suspension rates are still less safe than others that serve students with similar backgrounds in similar neighborhoods. At worst, this suggests that suspensions themselves can aggravate problems with safety. . . . Through their disciplinary practices, schools serving students from high-crime/high-poverty neighborhoods might unwittingly be exacerbating their low levels of safety. 155Id.

Moreover, empirical evidence also suggests that over-reliance on exclusionary discipline may not improve the academic achievement of the non-suspended students who remain in school. Brea Perry and Edward Morris empirically assessed the relationship between exclusionary discipline practices and student achievement, examining data from approximately 17,000 students attending seventeen secondary schools over six semesters. 156Brea L. Perry & Edward W. Morris, Suspending Progress: Collateral Consequences of Exclusionary Punishment in Public Schools, 79 Am. Soc. Rev. 1067, 1072 (2014). Their analysis revealed that higher levels of exclusionary discipline were associated with lower levels of math and reading achievement for non-suspended students, even after controlling for other factors that explain poor student achievement, such as levels of school violence and disorder and school demographic information. 157Id. at 1076–84. According to Perry and Morris, their findings support the theory that an overly punitive school environment destabilizes the school community, heightens students’ distrust and anxiety, and stifles achievement for all students. 158Id. at 1083.

B. The Effectiveness of Strict Security Measures in Creating Safe Learning Environments Is Far from Clear

Proponents of strict security measures contend that student safety outweighs all other concerns associated with their use. However, there are serious questions regarding whether strict security measures promote school safety, provide only a false sense of security, or actually lead to more school disorder. 159Advancement Project, Education on Lockdown: The Schoolhouse to Jailhouse Track 8 (2005), http://www.advancementproject.org/sites/default/files/publications/FINALEOLrep.pdf (arguing that strict security measures may “produce a perception of safety, [but] there is little or no evidence that they create safer learning environments or change disruptive behaviors”); Ascher, supra note 120, at 4 (“Rather than offering reassurance, metal detectors and other mechanical devices, as well as security forces, are seen as providing a false sense of safety, if not a harsh symbol of the failure to create safe schools.”); Mowen, Brent & Kupchik, supra note 80, at 443 (“[T]here is no clear evidence that the criminalization of school discipline is effective at preventing school violence.”); Richard E. Redding & Sarah M. Shalf, The Legal Context of School Violence: The Effectiveness of Federal, State, and Local Law Enforcement Efforts to Reduce Gun Violence in Schools, 23 Law & Pol’y 297, 319 (2001) (explaining that “[i]t is hard to find anything better than anecdotal evidence” showing that strict security measures such as metal detectors and guards reduce violence in schools); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 106–07. Notably, research on the effectiveness of school security measures is extremely limited, especially causal research. See Mayer & Leone, supra note 82, at 12. Abigail Hankin and her colleagues reviewed scholarship examining whether metal detectors create safer school environments. 160Abigail Hankin, Marci Hertz & Thomas Simon, Impacts of Metal Detector Use in Schools: Insights from 15 Years of Research, 81 J. Sch. Health 100, 105 (2011). They concluded that there was “insufficient evidence to draw a conclusion about the potential beneficial effect of metal detector use on student and staff behavior or perceptions.” 161Id. They also found that some of the research suggested that metal detector use was positively related to higher levels of school disorder and lower levels of students’ perceptions of school safety. 162Id.; see also John Blosnich & Robert Bossarte, Low-Level Violence in Schools: Is There an Association Between School Safety Measures and Peer Victimization?, 81 J. Sch. Health 107 (2011) (finding that school security measures did not reduce violent behaviors related to bullying); Matthew J. Mayer & Peter E. Leone, A Structural Analysis of School Violence and Disruption: Implications for Creating Safer Schools, 22 Educ. & Treatment Child. 333, 350, 352 (1999) (finding that student victimization and school disorder were higher in schools using strict security measures). But see Ctrs. for Disease Control & Prevention, Violence-Related Attitudes and Behaviors of High School Students—New York City, 1992, 42 Morbidity & Mortality Wkly. Rep. 773, 774, 776 (1993) (reporting that students who attended schools using metal detectors were less likely to carry a weapon inside a school—7.8% versus 13.6%—but the use of metal detectors did not reduce school violence); Renee Wilson-Brewer & Howard Spivak, Violence Prevention in Schools and Other Community Settings: The Pediatrician as Initiator, Educator, Collaborator, and Advocate, 94 Pediatrics 623, 626–27 (1994) (stating that one school system in New York City reported that after the school security staff began using hand-held metal detectors to conduct unannounced lobby searches of students at the beginning of the school day, weapon-related incidents decreased in thirteen of fifteen schools). Crystal Garcia reported that only 32% of school safety officers she interviewed believed that weapon detection systems effectively prevented or minimized violence crimes in schools. 163Crystal A. Garcia, School Safety Technology in American: Current Use and Perceived Effectiveness, 14 Crim. Just. Pol’y Rev. 30, 40 (2003).

Not surprisingly, violent incidents continue to occur in schools that rely on strict security measures, demonstrating that these measures cannot prevent all individuals from inflicting harm on members of the school community if they are determined to do so. 164Nance, School Surveillance and the Fourth Amendment, supra note 1, at 107–08; Nance, Students, Security, and Race, supra note 1, at 23–24. For example, the acts of school violence at Columbine High School occurred notwithstanding the presence of armed guards. 165See Marcus Wright, Experts Say Intrusive Security at Public Schools Reproduces Social Inequality, Truthout (Nov. 21, 2012, 11:22 AM), http://www.truth-out.org/news/item/12886-experts-say-intrusive-security-at-public-schools-reproduces-social-inequality. In 2005, a student shot another student in a high school with perimeter fencing, guards, and metal detectors. 166Sara Neufeld & Sumathi Reddy, Violent Week Renews Metal Detector Debate, Balt. Sun (Oct. 14, 2006), http://articles.baltimoresun.com/2006-10-14/news/0610140131_1_metal-detectors-school-students-park-elementary-school. Scholars and commentators observe that students understand how to bring weapons into schools without being detected, even in schools where strict security measures are present. 167See Ascher, supra note 120, at 5 (“[T]hose few students intent on bringing in weapons are inevitably a step ahead of the security devices, which means that enforcement activities alone cannot create a safe school.”); Neufeld & Reddy, supra note 166 (reporting that students interviewed claimed that it was “easy to get around” metal detectors); Noguera, supra note 120, at 193 (reporting that the students he spoke with understood how to bring a weapon into a school using strict security measures without detection); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 107–08; Nance, Students, Security, and Race, supra note 1, at 23–24. Ronald Stevens, an executive director of the National School Safety Center, maintains that strict security measures merely provide a false sense of security because “rule-followers will follow the rules,” and “[r]ule-breakers will break the rules.” 168Neufeld & Reddy, supra note 166.

Furthermore, some scholars believe that instead of helping to create safe learning environments, strict security measures actually hinder educators’ efforts because those measures can engender mistrust, alienation, resentment, and resistance among students, which may lead to even more disorder in schools. 169See Kupchik, supra note 80, at 7–8 (maintaining that punitive measures may increase rather than decrease student misbehavior); Ascher, supra note 120, at 5 (observing that strict security measures “increase, rather than alleviate, tension in schools”); Beger, supra note 81, at 340 (explaining that “aggressive security measures produce alienation and mistrust among students”); Michael Easterbrook, Taking Aim at Violence, 32 Psychol. Today, July/Aug. 1999, at 52, 56 (arguing that strict security measures alienate students); Clifford H. Edwards, Student Violence and the Moral Dimensions of Education, 38 Psychol. Schs. 249, 250 (2001) (observing that “intrusive strategies are likely to undermine the trust needed to build cooperative school communities capable of really preventing violence”); Mayer & Leone, supra note 162, at 349–50, 352 (finding that student disorder and victimization were higher in schools using strict security measures and arguing that “an unwelcoming, almost jail-like, heavily scrutinized environment[] may foster the violence and disorder school administrators hope to avoid”); Amanda B. Nickerson & Matthew P. Martens, School Violence: Associations with Control, Security/Enforcement, Educational/Therapeutic Approaches, and Demographic Factors, 37 Sch. Psychol. Rev. 228, 238–39 (2008) (finding that strict security measures and punitive measures were positively associated with more school disorder and school crime); Noguera, supra note 120, at 190–91 (observing that a “get-tough” approach can hamper school officials’ efforts to create safe environments because coercive measures creates mistrust and resistance among the student body); Noguera, supra note 81, at 345 (“When children are presumed to be wild, uncontrollable, and potentially dangerous, it is not surprising that antagonistic relations with the adults who are assigned to control them develop.”); Christopher J. Schreck, J. Mitchell Miller & Chris L. Gibson, Trouble in the School Yard: A Study of the Risk Factors of Victimization at School, 49 Crime & Delinq. 460, 471 (2003) (empirically finding that students attending schools that undertake locker searches report more student victimization); see also Nance, Random, Suspicionless Searches, supra note 1, at 399; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 106–07; Nance, Students, Security, and Race, supra note 1, at 21–22. The association between an intense, punitive environment and misbehavior is consistent with a social cognitive theory called “reciprocal determinism.” 170See Servoss, supra note 128, at 5; see also Albert Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory 27, 30 (1986); Albert Bandura, The Self System in Reciprocal Determinism, 33 Am. Psychol. 344, 356–57 (1978). Reciprocal determinism posits that a person’s behavior and the environment reciprocally influence one another. 171See Bandura, supra note 170; Servoss, supra note 128, at 5. Accordingly, as Timothy Servoss explains, while “student misbehavior may cause rules to tighten and security measures to be imposed,” a more likely explanation is that “inflexible environments and the perceptions that accompany them . . . promote misbehavior.” 172See Servoss, supra note 128, at 5.

Moreover, intense surveillance measures do not address the underlying problems associated with student crime and misbehavior; thus, those measures do not support long-term solutions to effectively prevent school violence. 173See Kupchik, supra note 80, at 6 (observing that schools do not often address the underlying issues associated with student misbehavior); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 108; Nance, Students, Security, and Race, supra note 1, at 24. For three years, the U.S. Department of Education and the U.S. Secret Service conducted a joint study to better understand how to more effectively prevent violent acts from occurring in schools. 174See Robert A. Fein et al., U.S. Secret Serv. & U.S. Dep’t of Educ., Threat Assessment in Schools: A Guide to Managing Threatening Situations and to Creating Safe School Climates, at iii (2004). They concluded that school climates that cultivate respect, provide emotional support, and pay attention to students’ academic, social, and emotional needs can reduce the possibility of targeted violence. 175Id. at 5–6. They also found that in safe schools, “adults and students respect each other;” “students develop the capacity to talk and openly share their concerns without fear of shame and reprisal;” “[s]tudents experience a sense of emotional ‘fit’ and of respect;” there are “positive personal role models in its faculty” and “place[s] for open discussion where diversity and differences are respected;” “communication between adults and students is encouraged and supported;” and “conflict is managed and mediated constructively.” 176Id. at 6, 11; see also Fedders, Langberg & Story, supra note 81, at 6 (“Positive relationships among students, families, teachers, administrators, and staff are the most effective tools in creating a safe school environment.”). In another study, Matthew Steinberg and his colleagues examined school safety in the Chicago Public School System and found that even in schools serving high concentrations of students from high poverty and crime areas, “it is the quality of relationships between staff and students and between staff and parents that most strongly defines safe schools. Indeed, disadvantaged schools with high-quality relationships actually feel safer than advantaged schools with low-quality relationships.” 177Steinberg, Allensworth & Johnson, supra note 105, at 1; see also Mark T. Greenberg et al., Enhancing School-Based Prevention and Youth Development Through Coordinated Social, Emotional, and Academic Learning, 58 Am. Psychol. 466, 468, 470 (2003) (finding that schools can successfully improve student behavior by creating caring communities, enhancing the school climate, and building trust among school staff, families, and students); Steinberg, Allensworth & Johnson, supra note 105, at 126 (reporting that school leadership, teacher collaboration, school-family interactions, and student-teacher relationships explained 80% of the variance associated with school safety as reported by students and teachers).

But, as explained above, strict security measures may hinder the development of respect, sense of emotional fit, and quality of relationships among members of the school community by fostering feelings of alienation, mistrust, resentment, adversity, and resistance among students. 178See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109; supra notes 120–33 and accompanying text. In addition, spending millions of dollars on intense surveillance measures diverts scarce funding from other evidence-based programs that may reduce school violence without degrading the learning environment. 179See Hankin, Hertz & Simon, supra note 160, at 105 (“Metal detector programs are expensive, and funds spent on metal detectors would not be available for other programs and strategies that have been shown to be effective at reducing youth risk for violence and promoting pro-social behaviors.”); Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109. As I discuss at length elsewhere, 180See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 345–60. there are several initiatives, such as restorative justice, 181See Thalia González, Socializing Schools: Addressing Racial Disparities in Discipline Through Restorative Justice, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 151, 151–53 (Daniel J. Losen ed., 2015); Restorative Discipline in Schools, Inst. for Restorative Just. & Restorative Dialogue, https://irjrd.org/restorative-discipline-in-schools/ (last visited July 26, 2016). Schoolwide Positive Behavioral Interventions and Supports, 182See Catherine P. Bradshaw, Mary M. Mitchell & Philip J. Leaf, Examining the Effects of Schoolwide Positive Behavioral Interventions and Supports on Student Outcomes, 12 J. Positive Behav. Interventions 133 (2010); What Is School-Wide PBIS, Positive Behav. Interventions & Supports, http://www.pbis.org/school/default.aspx (last visited Feb. 17, 2016); see also Mayer & Leone, supra note 82, at 13 (maintaining that Schoolwide Positive Behavioral Support can “transform[] the school environment to support overall student success, behaviorally, socially, and academically”). social and emotional learning, 183Joseph A. Durlak et al., The Impact of Enhancing Students’ Social and Emotional Learning: A Meta-Analysis of School-Based Universal Interventions, 82 Child Dev. 405, 406 (2011); David M. Osher et al., Avoid Quick Fixes: Lessons Learned from a Comprehensive Districtwide Approach to Improve Conditions for Learning, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 192, 192 (Daniel J. Losen ed., 2015). and improving classroom instruction and management skills of teachers, 184Michael Eskenazi, Gillian Eddins & John M. Beam, Equity or Exclusion: The Dynamics of Resources, Demographics, and Behavior in the New York City Public Schools 2 (2003) (maintaining that teacher qualifications have a strong positive effect on student behavior); Fedders, Langberg & Story, supra note 81, at 8; Daniel J. Losen, The Civil Rights Project, Discipline Policies, Successful Schools, and Racial Justice 1 (2011); Daniel J. Losen & Jonathan Gillespie, The Civil Rights Project, Opportunities Suspended: The Disparate Impact of Disciplinary Exclusion from School 35–37 (2012); Steinberg, Allensworth & Johnson, supra note 105, at 47–48; Anne Gregory et al., The Promise of a Teacher Professional Development Program in Reducing Racial Disparity in Classroom Exclusionary Discipline, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 166, 166–67 (Daniel J. Losen ed., 2015) (explaining that good teachers “are able to diffuse disruptive and disobedient behavior quickly, without relying on an office discipline referral that excludes a student from the classroom,” and “[r]esearch has shown that engaging and motivating teachers can prevent students from disrupting class in the first place”); David Osher et al., How Can We Improve School Discipline?, 39 Educ. Researcher 48, 49 (2010). that more effectively promote orderly, safe environments conducive to learning. These strategies may take time and concerted effort to implement effectively, but the empirical evidence demonstrates that their effective implementation promotes safety, enhances the learning climate, and improves student behavior and academic achievement more than strict, punitive measures ever could. 185See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 338–52; Nance, Students, Security, and Race, supra note 1, at 105–10.

C. The Disproportionate Use of Strict Security Measures on Students of Color Is Particularly Harmful

The fact that students of color are more likely to be subjected to intense school surveillance measures is particularly troubling for several reasons. 186Nance, Random, Suspicionless Searches, supra note 1, at 402–04; Nance, School Security Considerations, supra note 1, at 106–07; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109–12. First, the disproportionate use of intense surveillance methods on students of color is fundamentally unfair and may perpetuate inequalities already present in our education system. 187See Aaron Kupchik & Geoff Ward, Race, Poverty, and Exclusionary School Security: An Empirical Analysis of U.S. Elementary, Middle, and High Schools, 12 Youth Violence & Juv. Just. 332, 337–38 (2014); Loïc Wacquant, Deadly Symbiosis: When Ghetto and Prison Meet and Mesh, 3 Punishment & Soc’y 95, 108 (2011) (arguing that inner-city, majority-minority schools operate not as institutions of education, but as institutions of confinement and control); see also Nance, Random, Suspicionless Searches, supra note 1, at 403–04; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109–10; Nance, Students, Security, and Race, supra note 1, at 25. Schools that focus on custody and control above all other concerns deprive minority students of quality educational experiences, hindering their ability to pursue future educational and employment opportunities. 188See Hirschfield, supra note 80, at 40 (arguing that the disproportionate use of intense surveillance methods prepares urban minority students to become prisoners, soldiers, or service sector workers); Kupchik & Ward, supra note 187, at 338 (“[M]arginalized youth are presumed to be young criminals and treated as such through exposure to the hard edge of exclusive practices (e.g., police surveillance and metal detectors), while youth with social, political, and cultural capital are presumed to be near normal and habituated for social absorption in their selective exposure to inclusive security regimes.”); see also Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110; Nance, Students, Security, and Race, supra note 1, at 25. Furthermore, as previously discussed, intense surveillance methods, especially when used in conjunction with other punitive measures such as zero-tolerance policies, are a component of the larger “school-to-prison pipeline” problem. 189See supra Part II.A; see also Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110. Thus, disproportionate exposure to intense surveillance methods may contribute to the vast racial inequalities in school discipline, academic achievement, and involvement in the justice system. 190See Finn & Servoss, supra note 37, at 53 (finding that security levels were positively associated with greater racial disparities in suspensions); Edward W. Morris & Brea L. Perry, The Punishment Gap: School Suspension and Racial Disparities in Achievement, 63 Soc. Probs. 68, 81–84 (2016) (providing empirical evidence that school suspensions contribute to racial inequalities in academic achievement); Servoss & Finn, supra note 141, at 82–83 (finding that higher levels of school surveillance were positively associated with higher student suspension rates).

A second problem associated with the disproportionate use of intense surveillance measures on minority students is that this trend may weaken their trust in government institutions and authority. 191See Noguera, supra note 81, at 343–44; see also Victor M. Rios, Punished: Policing the Lives of Black and Latino Boys, at xiv, 75 (2011) (observing that students of color experience disproportionate surveillance because “schools, police, probation officers, families, community centers, the media, businesses, and other institutions systematically treat young people’s everyday behaviors as criminal activity,” making them feel “criminalized from a young age”); Khan, supra note 7 (reporting that students of color often perceive their school simply as an extension of a “police state” because they are subject to intense surveillance environments both in their neighborhoods and in their schools); Nance, Random, Suspicionless Searches, supra note 1, at 403–04; Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110; Nance, Students, Security, and Race, supra note 1, at 25–26. Linda Darling-Hammond maintains that young students of color are very observant of inequitable patterns along racial lines. She writes:

They note these patterns, and they understand when they have been identified as not deserving a high-quality, humane education. It is little wonder that in settings like these, students of color may come to doubt their academic ability and distrust the school, ultimately rejecting what it has to offer. 192Darling-Hammond, supra note 6, at 65.

Minerva Dickson’s experience exemplifies this point. When Minerva discovered that a student attending another school was not subject to the intense surveillance conditions she faced each day, she was shocked and dismayed. 193See Khan, supra note 7. She stated, “I thought all schools were like mine . . . I couldn’t believe a student could just walk into their school without dealing with all that.” 194Id.; cf. Nance, Persisting Inequalities, supra note 3 (reporting that when low-income minority students who attended low-resourced schools were shown photos of high-resourced schools, they responded with comments such as, “[t]hose must be schools for white kids,” and “[t]hey wouldn’t give those materials to us”). Trust between students of color and educators already is strained. 195See Constance Flanagan et al., School and Community Climates and Civic Commitments: Patterns for Ethnic Minority and Majority Students, 99 J. Educ. Psychol. 421, 423 (2007) (observing that studies show that minority groups report “a lower sense of school belonging than . . . their European American peers”); Susan Rakosi Rosenbloom & Niobe Way, Experiences of Discrimination Among African American, Asian American, and Latino Adolescents in an Urban High School, 35 Youth & Soc. 420, 434 (2004) (stating that “[w]hen African American and Latino students were asked about their experiences with discrimination, they described hostile relationships with adults in positions of authority such as . . . teachers in school”); Rosa Hernández Sheets, Urban Classroom Conflict: Student-Teacher Perception: Ethnic Integrity, Solidarity, and Resistance, 28 Urb. Rev. 165, 175 (1996) (reporting that minority students in a study on classroom conflict believed that their teachers did not care about them or respect them and that they abused their authority); see also Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 111; Nance, Students, Security, and Race, supra note 1, at 26–27; cf. Noguera, supra note 120, at 201 (observing that many black communities believe that black children are not treated fairly in schools). The disproportionate use of security measures may further impair the trust needed to establish positive, safe learning environments. 196See supra notes 120–33 and accompanying text; see also Kupchik & Ward, supra note 187, at 333; Nance, Random, Suspicionless Searches, supra note 1, at 403; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 111–12; Nance, Students, Security, and Race, supra note 1, at 26–27.

Third, the disproportionate use of security measures skews minority students’ perceptions of their standing and future roles in our society. 197See Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110–11; Nance, Students, Security, and Race, supra note 1, at 25–26. Scholars and youth advocates maintain that how we treat students affects how students perceive themselves, how they act, and who they will eventually become. 198See, e.g., Ronnie Casella, Being Down: Challenging Violence in Urban Schools 71–72 (2001) (arguing that as students “are deemed deviant, they are treated as deviants, and therefore deviate as is expected of them”); J. Alleyne Johnson, Life After Death: Critical Pedagogy in an Urban Classroom, 65 Harv. Educ. Rev. 213, 220–21 (1995) (observing how students’ self-perceptions changed for the worse upon being assigned to a special class once the students learned that the general perception of the class was that it was for students who were “at risk,” “learning disabled,” and “disruptive”). According to Pedro Noguera, when we label students as “defiant, maladjusted, and difficult to deal with . . . they are more likely to internalize these labels and act out in ways that match the expectations that have been set for them.” 199Noguera, supra note 81, at 343 (citations omitted). Thus, as Henry Leonardatos, an experienced school administrator in urban schools, points out, if we treat minority students like criminals, they begin to act like criminals. 200Khan, supra note 7.

Fourth, the disproportionate use of intense surveillance methods may contribute to the racial divide in this nation by sending incorrect and socially harmful messages to both white students and students of color. The disparate use of strict security measures signals to students that white students are privileged and have greater privacy rights while students of color cannot be trusted. 201See Nance, Random, Suspicionless Searches, supra note 1, at 425–26. This is exactly the wrong message to send children and is contrary to the values that public education should uphold. 202Cf. Nance, Persisting Inequalities, supra note 3 (arguing that tolerating inequalities with respect to school resources along racial lines sends minority students a message inconsistent with the values that public education should uphold). Rather, schools can and should play a critical role in mending racial divisions by teaching students “racial literacy” 203See Lani Guinier, From Racial Liberalism to Racial Literacy: Brown v. Board of Education and the Interest-Divergence Dilemma, 91 J. Am. Hist. 92, 113–16 (2004). or “race-relations intelligence,” 204Sharon E. Rush, Huck Finn’s “Hidden Lessons”: Teaching and Learning Across the Color Line 123–24 (2006) (emphasis omitted). which includes conveying in word and deed that all students are entitled to equal respect and dignity. 205See id. at 124; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 351–52. Sharon Rush explains:

Our children are watching us. They learn about race and race relations from us. As adults, we must be careful not to promote a vision of social reality that teaches non-White children that they are racially inferior or that teaches White children that they are racially superior. 206Sharon Elizabeth Rush, The Heart of Equal Protection: Education and Race, 23 N.Y.U. Rev. L. & Soc. Change 1, 33, 42 (1997).

III. Empirical Evidence Demonstrating Racial Disparities in the Application of Strict Security Measures

The unequal treatment of minority students has been repeatedly documented in almost all areas of public education, 207See, e.g., supra notes 3–6 and accompanying text. and this appalling trend also permeates the area of strict security measures in schools. This section will discuss several empirical studies that elucidate the disproportionate use of strict security measures on minority students, including an original empirical analysis of recent, restricted data that the U.S. Department of Education released after the shootings at Sandy Hook Elementary School.

A. 2009–2010 SSOCS Study

In 2013, I empirically tested the hypothesis that schools with higher concentrations of minority students were associated with greater odds of relying on intense surveillance methods than schools with lower concentrations of minority students, even after controlling for other factors that might influence school officials’ decisions to implement such measures. 208See Nance, Students, Security, and Race, supra note 1, at 27–43. Students, Security, and Race contains a detailed description of the study’s dataset, variables, and results, as well as its limitations. Here, I provide only a brief description of this study. To test this hypothesis, I analyzed restricted data from the National Center for Educational Statistics’ (NCES) 209The NCES “is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S. Department of Education and the Institute of Education Sciences.” About Us, Nat’l Ctr. Educ. Stat., https://nces.ed.gov/about/ (last visited Jan. 5, 2016). 2009–2010 School Survey on Crime and Safety (2009–2010 SSOCS), a national dataset containing information about school security practices, school crime, student demographics, neighborhood crime, and school disorder. 210See U.S. Dep’t of Educ. Nat’l Ctr. for Educ. Statistics, School Survey on Crime and Safety Principal Questionnaire: 2009–10 School Year [hereinafter 2009–2010 SSOCS Questionnaire], http://nces.ed.gov/surveys/ssocs/pdf/SSOCS_2010_Questionnaire.pdf (last visited Feb. 22, 2016); see also Nance, Students, Security, and Race, supra note 1, at 28. The dataset I analyzed was the restricted-access version. The restricted-access version contains sensitive information submitted by school officials about crimes that occurred in their schools or on their school grounds. See Statistical Standards Program: Getting Started, Nat’l Ctr. Educ. Statistics, http://nces.ed.gov/statprog/instruct_gettingstarted.asp (last visited Feb. 22, 2016). Although restricted datasets are available only to researchers who meet certain conditions, see Statistical Standards Program: Getting Started, supra, datasets containing less sensitive data are available to the public. See School Survey on Crime and Safety (SSOCS), Nat’l Ctr. Educ. Stat., http://nces.ed.gov/surveys/ssocs/data_products.asp (last visited Feb. 22, 2016); see also Nance, Students, Security, and Race, supra note 1, at 29. The NCES randomly selected 3480 schools to participate in the 2009–2010 SSOCS study. 211Nat’l Ctr. for Educ. Statistics, 2009–2010 School Survey on Crime and Safety (SSOCS): Restricted-Use Data File User Manual 8 (2011) [hereinafter 2009–2010 Restricted-Use Manual] (on file with author). One of the conditions imposed by NCES is that all raw numbers be rounded to the nearest ten. U.S. Dep’t of Educ., Restricted-Use Data Procedures Manual 20 (2011), http://nces.ed.gov/pubs96/96860rev.pdf. Approximately 2650 public schools throughout the nation submitted usable questionnaires (a 76% return rate). 2122009–2010 Restricted-Use Manual, supra note 211, at 1, 9–13.

The 2009–2010 SSOCS asked school principals to respond to several questions relating their school security practices. Principals indicated whether “[d]uring the 2009–10 school year . . . it [was] a practice of [their] school to”: “[r]equire students to pass through metal detectors each day”; “[p]erform one or more random metal detector checks on students”; “[p]erform one or more random sweeps for contraband (e.g., drugs or weapons), but not including dog sniffs”; “[c]ontrol access to school grounds during school hours (e.g., locked or monitored gates)”; “[u]se one or more security cameras to monitor the school”; and “have any security guards, security personnel, or sworn law enforcement officers present at [the] school at least once a week.” 2132009–2010 SSOCS Questionnaire, supra note 210, at 5, 8. The dependent variables for my study represented the odds that a school principal responded affirmatively to using various combinations of these surveillance practices. 214I examined four different combinations of security practices: (1) metal detectors and guards/school police; (2) metal detectors, guards/school police, and random sweeps for contraband; (3) metal detectors, guards/school police, random sweeps for contraband, and security cameras; and (4) metal detectors, guards/school police, random sweeps for contraband, security cameras, and locked gates. Nance, Students, Security, and Race, supra note 1, at 31. I note here one significant limitation of using the SSOCS data to test my hypothesis. In two of the survey questions, principals indicated whether it was a practice in their schools to perform “one or more random metal detector checks on students” or “random sweeps for contraband” during the 2009–2010 school year. 2009–2010 SSOCS Questionnaire, supra note 210, at 5. Thus, it is possible that by responding affirmatively to either (or both) of these survey items, the school personnel in fact performed only one random metal detector check or one random sweep for contraband during the school year. And if the school performed only one metal detector check or one random sweep during the school year, it is more difficult to make the argument that such actions create an intense surveillance environment. Nevertheless, if school officials indicate that they relied on several of these surveillance methods during the school year it seems more likely that they relied on these measures more than one time. Further, if school officials have access to equipment to perform random metal detector checks and use it at least once during the school year, it seems quite plausible that school officials used this equipment more than once (and used it regularly). Nance, Students, Security, and Race, supra note 1, at 32. The NCES should craft more careful questions to resolve this ambiguity in future questionnaires.

To measure the effect of student race, I included as an independent variable the percentage of minority students attending the schools. 215Nance, Students, Security, and Race, supra note 1, at 32. A school’s student minority population included students who were African-American, Hispanic, Asian/Pacific Islanders, and American-Indian/Alaska native students. See Simone Robers et al., Indicators of School Crime and Safety: 2011, at 112 tbl.6 (2012), http://www.bjs.gov/content/pub/pdf/iscs11.pdf. I also included other student characteristic and demographic information, such as student poverty (measured by the percentage of students who were eligible for free or reduced-price lunch); 216Nance, Students, Security, and Race, supra note 1, at 32. Free or reduced price lunch is a common proxy for measuring student poverty. See, e.g., Michael Heise, Litigated Learning, Law’s Limits, and Urban School Reform Challenges, 85 N.C. L. Rev. 1419, 1441 (2007); see also Nutrition and Schools, New Am., http://atlas.newamerica.org/federal-school-nutrition-programs (last visited July 14, 2016) (“Researchers often use free or reduced-price lunch (FRPL) enrollment figures as a proxy for poverty at the school level, because Census poverty data is not available disaggregated below the school district level and is not collected annually.”). the percentage of students enrolled in special education; the percentage of students with limited English proficiency; and the percentage of students who scored in the bottom 15% on the state standardized exam. 217Nance, Students, Security, and Race, supra note 1, at 33.

In addition, I controlled for school crime, which also might affect school officials’ decisions to rely on strict security measures. 218Id.; see also Kelly Welch & Allison Ann Payne, Racial Threat and Punitive School Discipline, 57 Soc. Probs. 25, 27 (2010) (“One factor presumed to be closely associated with school punitiveness and disciplinary practice is the level of school crime and disorder.”). The 2009–2010 SSOCS asked school principals to report the number of incidents of school crime by type that occurred at school or on school property during the school year. 2192009–2010 SSOCS Questionnaire, supra note 210, at 11. As the types of crimes might influence school officials’ decisions to implement stricter security controls, I categorized the wrongdoing. 220See Nance, Students, Security, and Race, supra note 1, at 33; see also Kupchik & Ward, supra note 187, at 341. I controlled for violent incidents; 221Violent incidents included robbery with or without a weapon, physical attack with or without a weapon, rape or attempted rape, and sexual battery other than rape. 2009–2010 SSOCS Questionnaire, supra note 210, at 11. threats of violence; 222Id. possession of a firearm, explosive device, knife, or other sharp object; 223Id. possession, distribution, or use of illegal drugs, inappropriate prescription drugs, or alcohol; 224Id. incidents of theft over $10; 225Id. and incidents of vandalism. 226Id.

Further, I controlled for the degree of overall disorder in the school as perceived by the school principal. 227Nance, Students, Security, and Race, supra note 1, at 33–34; see also Kupchik & Ward, supra note 187, at 341–42; Welch & Payne, supra note 218, at 27. To control for school disorder, I created an index based on responses to various questions in the 2009–2010 SSOCS about school disciplinary problems. For example, the 2009–2010 SSOCS asked school principals to rate on a scale of one to five the frequency of occurrences with respect to “student racial/ethnic tensions,” “student bullying,” “student sexual harassment of other students,” “student harassment of other students based on sexual orientation or gender identity (i.e., lesbian, gay, bisexual, transgender, questioning),” “widespread disorder in classrooms,” “student verbal abuse of teachers,” “student acts of disrespect for teachers other than verbal abuse,” “gang activities,” and “cult or extremist group activities.” 2282009–2010 SSOCS Questionnaire, supra note 210, at 13. Principals responded whether these types of problems happened daily, at least once a week, at least once a month, on occasion, or never. Id. I created my index by recoding the scale so that the higher values indicated greater frequency and computed the mean value of the principals’ responses. Nance, Students, Security, and Race, supra note 1, at 34; see also Kupchik & Ward, supra note 187, at 341–42.

Next, I controlled for the principal’s perception of crime in the area where the principal’s school resides. 229Nance, Students, Security, and Race, supra note 1, at 34. The 2009–2010 SSOCS asked school officials to “describe the crime level in the area where [the] school is located” on a scale of one to three (high, moderate, or low). 2302009–2010 SSOCS Questionnaire, supra note 210, at 17. I also controlled for community and external groups’ involvement in the school’s efforts to promote school safety, including involvement of parent groups, social services agencies, juvenile justice agencies, law enforcement agencies, mental health agencies, civic organizations/service clubs, private corporations/businesses, and religious organizations. 231Nance, Students, Security, and Race, supra note 1, at 34. Further, I controlled for the geographic region of the state in which the school is located (Northeastern State, Southern State, Western State, or Midwestern State); 232Id. at 35. whether the school was located in a city, suburb, town, or rural area; 233Id. the school’s total student enrollment; 234Id. the building level; 235Id. I examined only secondary schools; accordingly, I controlled for whether the school was a middle school, defined as a “school[] in which the lowest grade is not lower than grade 4 and the highest grade is not higher than grade 9,” a high school, defined as a “school[] in which the lowest grade is not lower than grade 9 and the highest grade is not higher than grade 12,” or a combined school, defined as “other combinations of grades, including K–12 schools.” Samantha Neiman & Monica R. Hill, Crime, Violence, Discipline, and Safety in U.S. Public Schools: Findings from the School Survey on Crime and Safety: 2009–10, at 7 tbl.1 n.5 (2011), http://nces.ed.gov/pubs2011/2011320.pdf. whether the school was a traditional school or nontraditional school (charter school or magnet school); 236Nance, Students, Security, and Race, supra note 1, at 35. and the school’s student attendance rate. 237Id. at 35–36.

All of the models suggest that higher concentrations of minority students are predictive of greater odds that schools subject students to the designated combinations of security measures. 238Id. at 40–41. Further, and more importantly, student race remained statistically significant even after controlling for the other factors described above, including the number of incidents of school crime, the principals’ perception of crime in the area in which the school resides, the principals’ perception of disorder at school, school location, and the total number of students enrolled at school. 239Id.

B. 2013–2014 SSOCS Study

1. Data and Sample

In 2015, the Department of Education released a new set of data, the 2013–2014 School Survey on Crime and Safety (2013–2014 SSOCS), providing fresh information on the security practices of public schools throughout the United States. 240See Lucinda Gray, Laurie Lewis & John Ralph, Public School Safety and Discipline: 2013–14 (2015), http://nces.ed.gov/pubs2015/2015051.pdf. This dataset is particularly interesting because it represents the first attempt to gather national data on school security practices since the shootings at Sandy Hook Elementary School on December 14, 2012, which sparked national concern over security practices in schools. 241See, e.g., Stephanie Banchero & Caroline Porter, In Pursuit of Safety, Schools’ Paths Diverge, Wall St. J. (Jan. 17, 2013, 7:49 PM), http://online.wsj.com/article/SB10001424127887323783704578248113754373942.html (“Schools are running security drills, planning to install security cameras and bullet-proof glass, and hiring safety consultants.”); Lynh Bui, Public Gets First Chance to Weigh in on Proposed FY2014 Budget for Montgomery Co. Schools, Wash. Post (Jan. 11, 2013), http://www.washingtonpost.com/blogs/maryland-schools-insider/post/montgomery-county-parents-ask-for-more-school-security-teacher-training-during-budget-hearing/2013/01/11/e8d3dcf4-5aab-11e2-9fa9-5fbdc9530eb9_blog.html (discussing the increased concerns parents have for their children’s safety and their demand for increased on-campus security); Motoko Rich, School Officials Look Again at Security Measures Once Dismissed, N.Y. Times (Dec. 18, 2012), http://www.nytimes.com/2012/12/19/education/after-newtown-shootings-schools-consider-armed-security-officers.html?pagewanted=1&_r=1 (discussing the reactions of policymakers to the shootings at Sandy Hook Elementary School). However, because of budget cuts, the NCES did not have the resources to conduct a full-scale survey as it had done in prior years. 242E-mail from Nat’l Ctr. for Educ. Statistics to Jason P. Nance, Assoc. Professor of Law, Univ. of Fla. Levin Coll. of Law (May 21, 2013, 2:41 PM) (on file with author). Accordingly, the NCES designed a smaller questionnaire and selected fewer schools to participate in the study. 243Gray, Lewis & Ralph, supra note 240, at 1. Thus, I was not able to control for the same factors I controlled for in the 2009–2010 study. Nevertheless, the 2013–2014 SSOCS still contains meaningful information worthy of analysis regarding the association between schools’ concentrations of minority students and school officials’ decisions to implement various combinations of security practices.

The NCES mailed the 2013–2014 SSOCS to approximately 1600 traditional public schools nationally, and approximately 1350 schools completed the survey. 244NCES reported an unweighted survey response rate of 86%. Id. The dataset I analyzed was the restricted-access version. 245See supra note 210 and accompanying text for a description of “restricted-use data.”

2. Dependent and Independent Variables

Regarding schools’ security practices, the 2013–2014 SSOCS followed the same format as the 2009–2010 SSOCS, asking school officials whether it was their practice to “[r]equire students to pass through metal detectors each day;” “[p]erform one or more random metal detector checks on students;” “[p]erform one or more random sweeps for contraband (e.g., drugs or weapons), but not including dog sniffs;” “[c]ontrol access to school grounds during school hours (e.g., locked or monitored gates);” “[u]se one or more security cameras to monitor the school;” and “have any security guards, security personnel, or sworn law enforcement officers present at [the] school at least once a week.” 246Gray, Lewis & Ralph, supra note 240, at C-4 to C-5. However, unlike the 2009–2010 SSOCS, which requested that the principal complete the survey, the 2013–2014 SSOCS requests that the “person(s) most knowledgeable about safety and discipline at [the] school” complete the survey. Id. at C-2. The dependent variables for my study represented the odds that a school relied on various combinations of these security practices. 247I created eight combinations of security measures: combination 1 includes metal detectors and SROs/guards; combination 2 includes metal detectors and random sweeps; combination 3 includes random sweeps and locked gates; combination 4 includes metal detectors and locked gates; combination 5 includes locked gates and SROs/guards; combination 6 includes random sweeps and SROs/guards; combination 7 includes metal detectors, SROs/guards, and random sweeps; and combination 8 includes metal detectors, locked gates, and SROs/guards. I included more combinations in the 2013–2014 SSOCS Study than in the 2009–2010 SSOCS Study to underscore the strong association between student race and strict security measures in a variety of contexts. However, unlike the 2009–2010 SSOCS Study, none of the combinations I included in the 2013–2014 SSOCS Study had more than three types of measures. The reason for the low number of measures is that the sample sizes in the 2013–2014 SSOCS Study were much smaller than the sample sizes in the 2009–2010 SSOCS Study, which significantly affected the statistical power of the analyses. That is, while several schools indicated that they relied on combinations of security measures greater than three (or other combinations of three not included in the study) and there were observable differences when I disaggregated the data by race, my empirical analyses did not have the statistical power to detect those racial differences to a statistically-significant degree.

The 2013–2014 SSOCS database did not include the percentage of minority students attending the schools. Rather, it contained only a categorical variable for student body race (% Minority Students). While this categorical variable is less robust than a continuous variable, it still provides a useful lens to examine the relationship between race and the use of security measures. I created dummy variables for schools with minority populations between 0% and 19% (0%–19% minority) and schools with minority populations between 20% and 49% (20%–49% minority) to compare to schools with minority populations of over 50%. 248Creating dummy variables is a way to include categorical predictor variables in estimation models such as logistic regression. See Inst. for Dig. Research & Educ., FAQ: What is Dummy Coding?, UCLA, http://www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm (last visited July 26, 2016).

As with the 2009–2010 SSOCS, I controlled for school crime. However, because the 2013–2014 SSOCS asked school officials to report fewer types of incidents than the 2009–2010 SSOCS study, 249Compare Gray, Lewis & Ralph, supra note 240, at C-6 (asking participants for information about five types of incidents), with 2009–2010 SSOCS Questionnaire, supra note 210, at 11 (asking participants for information about twelve types of incidents). I created only two categories: (1) incidents involving weapons or sexual battery (weapon/sex incidents), 250This category included incidents such as rape, sexual battery other than rape, robbery with a weapon, physical attack with a weapon, and threat of physical attack with a weapon. Gray, Lewis & Ralph, supra note 240, at C-6. and (2) all other incidents not involving weapons or sexual battery (non-weapon/non-sex incidents). 251This category included incidents such as robbery without a weapon, physical attack without a weapon, and threat of physical attack without a weapon. Id. While I could have categorized incidents in other ways, I chose to categorize them in this manner because incidents involving weapons and sexual battery tend to be much more serious than the other listed offenses. Because both of these variables were positively skewed, I transformed them by including their natural logs. 252See Kupchik & Ward, supra note 187, at 342 (transforming continuous variables by including their natural logs to correct for positive skewness); Nance, Students, Security, and Race, supra note 1, at 36.

Next, similar to the 2009–2010 SSOCS, I controlled for the degree of overall disorder in the school. I created an index based on responses to questions asking for the frequency of occurrences relating to “[s]tudent racial/ethnic tensions,” “[s]tudent bullying,” “[s]tudent sexual harassment of other students,” “[s]tudent harassment of other students based on sexual orientation or gender identity (i.e., lesbian, gay, bisexual, transgender, questioning),” “[w]idespread disorder in classrooms,” “[s]tudent verbal abuse of teachers,” and “[s]tudent acts of disrespect for teachers other than verbal abuse.” 253Gray, Lewis & Ralph, supra note 240, at C-6. Unlike the 2009–2010 SSOCS, the 2013–2014 SSOCS did not ask survey participants about gang activities or cult or extremist group activities at school. Compare id. (not asking participants for information about gang, cult, and extremist activities), with 2009–2010 SSOCS Questionnaire, supra note 210, at 12–13 (asking participants for information about gang, cult, and extremist activities).

Finally, I controlled for the geographic region of the state in which the school is located, urbanicity, building level, and total student enrollment. 254Unfortunately, unlike the 2009–2010 SSOCS, the 2013–2014 SSOCS did not provide information on student poverty; neighborhood crime; the percentage of students enrolled in special education; the percentage of students with limited English proficiency; the percentage of students who scored in the bottom 15% on state standardized exams; and daily attendance. Compare 2009–2010 SSOCS Questionnaire supra note 210, at 16–17 (asking participants for this information), with Gray, supra note 240, at C-2 to C-6 (not asking participants to submit this information). I controlled for the geographic region of the school by creating dummy variables for schools in northeastern states, western states, and midwestern states to compare to schools in southern states. I controlled for urbanicity by creating dummy variables for schools located in suburbs, towns, or rural areas to compare to schools located in cities. I controlled for school building level by creating a dummy variable for middle schools to compare to high schools / combined schools. 255See Nance, Students, Security, and Race, supra note 1, at 35. I examined only secondary schools. Accordingly, I controlled for whether the school was a middle school, defined as a school “in which the lowest grade is greater than or equal to 4 and the highest grade is less than or equal to 9,” a high school, defined as a school “in which the lowest grade is greater than or equal to 9 and the highest grade is less than or equal to 12,” or a combined school, defined as a school “with all other combinations of grades, including K–12.” Gray, Lewis & Ralph, supra note 240, at 2 n.3. Unlike the 2009–2010 SSOCS, the 2013–2014 SSOCS merged high schools and combined schools into one category. See Neiman & Hill, supra note 235, at 1, 7 tbl.1 (listing combined schools and high schools as separate categories); see also Gray, Lewis & Ralph, supra note 240, at 2 n.3 (combining high schools and combined schools into one data category). And I controlled for total student enrollment by creating dummy variables for schools with less than 300 students and schools with 300–499 students to compare to schools with 500 or more students. 256The 2009–2010 SSOCS database provided a continuous variable for student population. See Nance, Students, Security, and Race, supra note 1, at 35.

3. Methodology

Each of the dependent variables represents whether a school employed various combinations of these security measures. If a school employed all of the methods in a certain combination, I assigned it a value of “1.” If it did not employ all of the methods in a certain combination, I assigned it a value of “0.” Because the dependent variables were dichotomous, I used binary logistic regression to estimate the odds that a school employed a specific combination of strict security measures. 257See generally Joseph F. Hair, Jr. et al., Multivariate Data Analysis 276–81 (5th ed. 1998) (providing an overview of logistic regression analysis). To produce national estimates, I used the weighted variable provided in the 2013–2014 SSOCS database. 258The weighted variable compensates for unequal probabilities of selection, minimizes bias associated with responding and nonresponding schools, reduces sampling error, and calibrates the data to known population characteristics to produce optimal national estimates. See Gray, Lewis & Ralph, supra note 240, at B-4 (describing the specific weighting procedures employed). Furthermore, I adjusted the sample weight by dividing it by its mean to create a mean weight of one. This is a recommended procedure when employing logistic regression analysis using SPSS. See Helen M. Marks & Jason P. Nance, Contexts of Accountability Under Systemic Reform: Implications for Principal Influence on Instruction and Supervision, 43 Educ. Admin. Q. 3, 14 (2007); Patty Glynn, Adjusting or Normalizing Weights “On the Fly” in SPSS, U. Wash., http://staff.washington.edu/glynn/adjspss.pdf (last updated July 8, 2004). As with the 2009–2010 SSOCS study, I evaluated only the secondary schools that participated in the study (approximately 410 middle schools and 490 high schools / combined schools, for an approximate total of 900 schools).

4. Results

Table 1 contains the results of the logistic regression analysis. It displays the exponentiated coefficients (Exp(B)) for the independent variables in each of the models. 259An exponentiated coefficient estimates the change in odds of a school using a certain combination of security practices for each one-unit increase in an independent variable. See Raymond E. Wright, Logistic Regression, in Reading and Understanding Multivariate Statistics 217, 223 (Laurence G. Grimm & Paul R. Yarnold eds., 1995). For categorical variables, it represents a change in the odds of a school using a certain combination of security practices when that condition is present compared to the specified comparison variable noted. See id. at 233. For example, the change in odds of using a specific combination of security practices for schools with minority populations of between 0%–19% verses schools with minority populations of over 50%. Table 1 also displays whether the effects of the independent variables are statistically significant. 260The variance inflation factors (VIF), a common statistic to detect multicollinearity, indicated that multicollinearity was not a problem for the models. Additionally, the Hosmer & Lemeshow test, a statistical test to assess a lack of fit between the data and the model, indicated a goodness of fit for each of the eight models except for the combination of measures that included random sweeps and locked gates (combination 3).

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FeIiwlTgePny5VZkycWwWvPVlStXijVuVDFQNprZjQMHPfhMWy7NG0ihkxkBI2AEjIARMAI1I7CK t4SDIm9GY2TyjRgOY5gsr0r78uULz7Xan4slbw8ePMC7Ck+I3Sn5l4AQM5bIHuvmtJiu57p06RJv mTtF4dSCh6SdKvG6tDrPlxEwAkbACBgBI2AEhiLQnGlPzpnT75mdrVVvXLEOTmNhscYtZnBzk9fE RUYS8yrSQ1I4OnqY967UwJ8ujdnxqxJ4RdX6lysW4s3kcans89FeipLNyjHLm0G9Y0WW8o7gb1a1 pbwZ1DtWZCkL/F/4KxwrYjnNh0OdL6cbicAFRNssj9SRQya3lA8ptpFEW8ojATtkcktZYltlJO6Q GmGijYARMAJGwAgYASPQhoC9JeuFETACRsAIGAEjYAT6ELC3ZP0wAkbACBgBI2AEjEAfAu3zloyZ ETACRsAIGAEjYAQuJgItU7o9oftiqoK5NgJGwAgYASNgBAYi4JG4gUA5mREwAkbACBgBI3BBEbC3 dEEFb7aNgBEwAkbACBiBgQjYWxoIlJMZASNgBIyAETACFxQBe0sXVPBm2wgYASNgBIyAERiIgL2l gUA5mREwAkbACBgBI3BBEbC3dEEFb7aNgBEwAkbACBiBgQj8v0j0KkVA1fXzAAAAAElFTkSuQmCCTable 1: Logistic Regression Model Predicting Odds of School Using a Combination of Security Practices (Exp(B) Reported)

*p < 0.10, ** p < 0.05, ***p < 0.01, ****p < 0.001

Notes: Combination 1 includes metal detectors and SROs/guards

Combination 2 includes metal detectors and random sweeps

Combination 3 includes random sweeps and locked gates

Combination 4 includes metal detectors and locked gates

Combination 5 includes locked gates and SROs/guards

Combination 6 includes random sweeps and SROs/guards

Combination 7 includes metal detectors, SROs/guards, and random sweeps

Combination 8 includes metal detectors, locked gates, and SROs/guards

a. Schools with minority populations of over 50% were the comparison group.

b. Schools located in Southern states were the comparison group.

c. Urban schools were the comparison group.

d. High schools were the comparison group.

e. Schools serving over 500 students were the comparison group.

Similar to the 2009–2010 SSOCS Study, all of the models suggest that higher concentrations of minority students are predictive of greater odds that schools rely on the designated combinations of security measures. Further, as with the 2009–2010 SSOCS Study, these associations still remain statistically significant even after controlling for other important factors such as school crime, school disorder, and urbanicity. In fact, the empirical models indicate that the odds of employing each of the eight combinations of security measures for schools with minority populations of over 50% range from two to as high as eighteen times greater than in schools serving minority populations of between 0% and 19%. 261When an exponentiated coefficient is less than one, the odds ratio is easier to interpret by dividing one by the exponentiated coefficient. See Michael H. Katz, Multivariable Analysis: A Practical Guide for Clinicians 130 (1999) (explaining a common procedure for computing an odds ratio for exponentiated coefficients less than one to facilitate interpretation). Following this procedure, the odds of relying on each combination of security measures for schools with minority populations of over 50% compared to schools serving minority populations of between 0% and 19% are as follows: 5.8 (combination 1); 2.8 (combination 2); 2.9 (combination 3); 18.1 (combination 4); 3.4 (combination 5); 2.0 (combination 6); 2.2 (combination 7); 15.1 (combination 8). Similarly, the odds of employing each of the eight combinations for schools with minority populations of over 50% range from 1.6 to 3.3 times greater when compared to schools with minority populations of between 20% and 49%. 262Specifically, the odds of relying on each combination of security measures for schools with minority populations of over 50% compared to schools serving minority populations of between 20% and 49% are as follows: 3.2 (combination 1); 2.7 (combination 2); 2.3 (combination 3); 3.3 (combination 4); 2.0 (combination 5); 1.6 (combination 6); 2.3 (combination 7); 3.0 (combination 8). Thus, consistent with the 2009–2010 SSOCS Study, the data suggest that schools with higher concentrations of minority students are more inclined to rely on heavy-handed measures to maintain order than other schools facing similar crime and discipline issues.

C. Other Empirical Studies

Other empirical studies also demonstrate a strong association between race and the use of strict security measures. For example, Jeremy Finn and Timothy Servoss examined the relationship between race and the use of security measures by examining data from the Educational Longitudinal Study of 2002, the Common Core of Data, and the Civil Rights Data Collection. 263See Finn & Servoss, supra note 37, at 46; Servoss & Finn, supra note 141, at 68–69. Finn and Servoss discovered that of all the school characteristics they studied, “[t]he strongest correlation was with the percentage of Black students in the school. That is, the percentage of Black students enrolled was more highly related to security levels than was any other characteristic,” 264Finn & Servoss, supra note 37, at 49; see also Servoss & Finn, supra note 141, at 79–80 (“In sum, a high proportion of Black students in a school is related to the degree of security the school implements above and beyond all other characteristics we studied.”). including neighborhood crime, the percentage of socioeconomically disadvantaged students, building level, the number of students enrolled, and urbanicity. 265Finn & Servoss, supra note 37, at 44, 49; see also Servoss & Finn, supra note 141, at 79–80. Further, the percentage of African-American students was significantly related to school security levels even after controlling for other school and student characteristics. 266Servoss & Finn, supra note 141, at 80. In another study that Timothy Servoss conducted using the Education Longitudinal Study of 2002, he found that “students in high security schools are 11.78 times more likely to be African American than White . . . , and 1.56 times more likely to be Hispanic/Latino than White.” Servoss, supra note 128, at 13; see also Ivory A. Toldson, Breaking Barriers 2: Plotting the Path Away from Juvenile Detention and Toward Academic Success for School-Age African American Males 7 (2011), http://cbcfinc.org/oUploadedFiles/BreakingBarriers2.pdf (finding that black students were approximately 4.8 times more likely to report passing through a metal detector when entering school than white students, and Latino students were approximately 2.65 times as likely to report passing through metal detectors when entering school than white students); see also Thomas J. Mowen & Karen F. Parker, Minority Threat and School Security: Assessing the Impact of Black and Hispanic Student Representation on School Security Measures, Security J. 1 (2014) (finding that the percentage of African-American students at a school was positively related to the use of strict security measures).

Karen DeAngelis and her colleagues examined financial data from Texas to assess how much school districts spend on school security and the extent to which spending differs across different types of school districts. 267Karen J. DeAngelis et al., The Hidden Cost of School Security, 36 J. Educ. Fin. 312 (2011). The state of Texas requires all districts to report security expenditures relating to security guards, hall monitors for security, security vehicles, and security equipment such as metal detectors and surveillance cameras. 268Id. at 318–19. Their empirical analysis revealed that after controlling for the urbanicity of the school districts, student enrollment, wealth of the district per average daily attendance, and student poverty, school districts serving higher concentrations of minority students tended to spend more on security measures than other school districts. 269Id. at 329. Furthermore, the data indicated that poorer school districts serving higher concentrations of low-income students and minority students tended to spend disproportionately more on school security than other districts. 270Id. at 329–31.

Still other empirical studies, although not directly related to the disparate use of security measures along racial lines, demonstrate that student race is strongly associated with the use of punitive disciplinary measures generally. For example, in one notable study examining a national sample of 294 schools, Kelly Welch and Allison Payne observed that schools serving greater percentages of African-American students were more likely to inflict harsher punishments for student misbehavior, such as suspension, expulsion, and referring students to law enforcement. 271Welch & Payne, supra note 218, at 36. They also found that schools with higher concentrations of African-American students were less likely to use softer disciplinary measures such as oral reprimands or referrals to visit with the school counselor. 272Id. Further, they found that schools with higher concentrations of African-American students were less supportive of restorative justice programs and alternative forms of discipline, such as assigning students to perform community service. 273Id.

In fact, racial disparities in student suspensions, expulsions, referrals to law enforcement, and school-based arrests have been documented repeatedly at the local, state, and national levels for decades. 274Russell J. Skiba, Mariella I. Arredondo & Natasha T. Williams, More Than a Metaphor: The Contribution of Exclusionary Discipline to a School-to-Prison Pipeline, 47 Equity & Excellence Educ. 546, 550 (2014); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 331–32; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 957. For example, the U.S. Department of Education’s Civil Rights Data Collection (CRD Collection) shows that although African-Americans comprised 16% of the student population in the 2011–2012 school year, they accounted for: 32% of students who received an in-school suspension; 33% of students who received one out-of-school suspension; 42% of students who received more than one out-of-school suspension; 34% of students who were expelled; 27% of students who were referred to law enforcement; and 31% of students who were subjected to a school-related arrest. 275See U.S. Dep’t of Educ. Office for Civil Rights, Civil Rights Data Collection, Data Snapshot: School Discipline 2, 6 (2014), [hereinafter Data Snapshot]; see also Losen & Gillespie, supra note 184, at 6 (observing that one out of every six African-American students enrolled in K–12 schools had been suspended at least once, but only one out of twenty white students had been suspended). Further, although African-American children comprised 18% of children enrolled in a pre-K program, they represented 48% of those pre-K children who received more than one out-of-school suspension. 276Data Snapshot, supra note 275, at 1. According to highlights of the 2013–2014 CRD Collection that the OCR released in June 2016, significant racial disparities still exist relating to school discipline. Office for Civil Rights, U.S. Dep’t of Educ., 2013–2014 Civil Rights Data Collection: A First Look 3 (2016). According to that data, African-American students are approximately 3.8 times more likely to receive one or more out-of-school suspensions than white students. Id. Further, although African-American children represented 19% of students enrolled in pre-school, they represented 47% of students receiving one or more out-of-school suspensions. Id. While some may posit that these racial disparities exist because of differences in behavior with respect to these student groups, the U.S. Department of Education’s Office of Civil Rights (OCR) stated that more frequent or serious misbehavior by minority students does not adequately explain these disparities. 277See Dear Colleague Letter, supra note 4, at 4. Rather, the OCR disturbingly confirmed that it has found “cases where African-American students were disciplined more harshly and more frequently because of their race than similarly situated white students. In short, racial discrimination in school discipline is a real problem.” 278Id. Indeed, there are multiple empirical studies that corroborate the OCR’s findings. 279See, e.g., Losen, supra note 184, at 6–7; Catherine P. Bradshaw et al., Multilevel Exploration of Factors Contributing to the Overrepresentation of Black Students in Office Disciplinary Referrals, 102 J. Educ. Psychol. 508, 513–14 (2010) (observing that, after controlling for teacher ratings of students’ behavior problems, African-American students were more likely than white students to be referred to the office for disciplinary reasons); Theresa Glennon, Looking for Air: Excavating Destructive Educational and Racial Policies to Build Successful School Communities, in Justice for Kids: Keeping Kids Out of the Juvenile Justice System 107, 110–11 (Nancy E. Dowd ed., 2011) (discussing studies demonstrating that minority students are disciplined disproportionately); Anna C. McFadden et al., A Study of Race and Gender Bias in the Punishment of Handicapped School Children, 24 Urb. Rev. 239, 246–47 (1992) (finding that African-American male disabled students were punished more severely than other students for the same offenses); Michael Rocque & Raymond Paternoster, Understanding the Antecedents of the “School-to-Jail” Link: The Relationship Between Race and School Discipline, 101 J. Crim. L. & Criminology 633, 651–54 (2011) (concluding that African-American students are significantly more likely than white students to be disciplined even after taking into account other salient factors such as grades, attitudes, gender, special education or language programs, and their conduct in school as perceived by teachers); Russell J. Skiba et al., Race Is Not Neutral: A National Investigation of African American and Latino Disproportionality in School Discipline, 40 Sch. Psychol. Rev. 85, 95–101 (2011) (observing significant racial disparities in school discipline after examining an extensive national sample); Russell J. Skiba et al., Where Should We Intervene? Contributions of Behavior, Student, and School Characteristics to Out-of-School Suspension, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 132, 132–35 (Daniel J. Losen ed., 2015) (finding that race was a strong predictor of out-of-school suspensions); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 332. The Discipline Disparity Collaborative sums it up this way:

The crux of the matter then, is whether Black students engage in more seriously disruptive behavior that could justify different rates and severity of consequences. A number of different methods have been used to test the idea that differential punishment is due to different rates of misbehavior. Regardless of the method, such studies have provided little to no evidence that African American students in the same school or district are engaging in more seriously disruptive behavior that could warrant higher rates of exclusion or punishment. 280Russell J. Skiba & Natasha T. Williams, Are Black Kids Worse? Myths and Facts About Racial Differences in Behavior 3 (2014).

Finally, one must remember that the empirical studies discussed above do not even begin to fully illuminate the overall problem of racial inequalities in our public school system. For example, minority students are overrepresented in restrictive special education programs 281Catherine Y. Kim, Daniel J. Losen & Damon T. Hewitt, The School-to-Prison Pipeline: Structuring Legal Reform 53–54 (2010); Pedro A. Noguera, The Trouble with Black Boys and Other Reflections on Race, Equity, and the Future of Public Education, at xvii (2008); Nancy E. Dowd, What Men?: The Essentialist Error of the “End of Men”, 93 B.U. L. Rev. 1205, 1216–22 (2013). and alternative schools, 282Angela A. Ciolfi & James E. Ryan, Race and Response-to-Intervention in Special Education, 54 How. L.J. 303, 326–27 (2011). are disproportionately retained from grade to grade, 283See, e.g., Susan Aud, Mary Ann Fox & Angelina KewalRamani, Status and Trends in the Education of Racial and Ethnic Groups, 92 tbl.17a (2010); Catherine E. Lhamon, Five New Facts from the Civil Rights Data Collection, U.S. Dep’t Educ.: Homeroom (Mar. 21, 2014), http://www.ed.gov/blog/2014/03/five-new-facts-from-the-civil-rights-data-collection/. and suffer from lower academic expectations from teachers. 284See, e.g., Sarah E. Redfield, Diversity Realized: Putting the Walk with the Talk for Diversity in the Legal Profession 72–76 (2009); Ronald F. Ferguson, Teachers’ Perceptions and Expectations and the Black-White Test Score Gap, 38 Urb. Educ. 460, 477–78 (2003). These studies also fall well short in conveying the vast inequalities that minority youth experience in areas outside of public education such as in the juvenile justice system. 285And, of course, they do not even scratch the surface of the inequalities present in our nation with respect to minorities generally relative to interactions with the police, our justice system, and employment, just to name a few. See, e.g., Deborah L. Rhode, From Platitudes to Priorities: Diversity and Gender Equity in Law Firms, 24 Geo. J. Legal Ethics 1041, 1045–46 (2011) (discussing racial inequalities for minorities in legal jobs); L. Song Richardson, Arrest Efficiency and the Fourth Amendment, 95 Minn. L. Rev. 2035, 2036–38 (2011) (providing evidence that demonstrates that although stops and searches of whites proportionately yield more illegal contraband, the police still stop and search African-Americans at higher rates); see generally Michelle Alexander, The New Jim Crow: Mass Incarceration in the Age of Colorblindness (2010) (discussing racial inequalities present throughout the justice system); Paul Butler, One Hundred Years of Race and Crime, 100 J. Crim. L. & Criminology 1043, 1045–46 (2010) (describing racial inequalities present throughout American society). For example, empirical studies show that minority youth, particularly African-Americans, are much more likely to be involved in and treated more harshly by the criminal justice system than similarly situated white youth. 286Nat’l Ctr. for Juvenile Justice, Juvenile Arrest Rates by Offense, Sex, and Race: 1980–2011 (2014), http://www.ojjdp.gov/ojstatbb/crime/excel/JAR_2011.xls; see also Dowd, supra note 281, at 1222–26 (providing evidence that African-American youth are overrepresented in the juvenile justice system and receive disparate and harsher treatment). They are disproportionately arrested, referred to juvenile justice court, adjudicated by juvenile court, detained in secure facilities, and sentenced to adult state prisons. 287Mark Soler, Dana Shoenberg & Marc Schindler, Juvenile Justice: Lessons for a New Era, 16 Geo. J. on Poverty L. & Pol’y 483, 530–31 (2009). In fact, in 2010, African-American youth were more likely to be detained than any other youth group for almost all categories of offenses. 288See Office of Juvenile Justice & Delinquency Prevention, Juvenile Offenders and Victims: 2014 National Report 163–64 (Melissa Sickmund & Charles Puzzanchera eds., 2014). The greatest disparity was for drug offenses, where African-Americans were almost twice as likely to be detained in drug cases as whites. Id. at 164.

IV. The Role of Implicit Racial Bias

The empirical studies discussed above point to a consistent finding: youth of color, especially African-Americans, are treated more harshly and poorly than similarly situated white youth in a variety of contexts. Notably, this finding also holds true for decisions that school officials make regarding the application of security measures. The empirical studies reveal that higher concentrations of minority students relate positively to the odds of subjecting students to various combinations of security measures, even after controlling for other salient factors such as neighborhood crime, school disorder, school crime, and other student demographic and school characteristic information. 289See supra Part III. In other words, the empirical studies suggest that student race influences school officials’ decisions to implement tighter security measures among the student body. 290I emphasize here that these empirical studies do not affirmatively establish that student race causes school officials to implement tighter security measures. These empirical studies only demonstrate a strong relationship between race and the decisions to implement strict security measures. More research must be conducted to establish causation.

The results of the empirical analyses raise questions about why these racial disparities exist, why our nation tolerates their existence in this area (and in so many other areas of public education and society), and what we can do to create more just and inclusive schools for students of all races. 291See Nance, Over-Disciplining Students, supra note 1, at 1067; Redfield, supra note 31. What is especially curious is that although some school officials and teachers may be motivated by racial animus in their decisionmaking, most school officials are probably trying to act in good faith much of the time. 292Nance, Over-Disciplining Students, supra note 1, at 1067; Redfield, supra note 31; see also Cheryl Staats, Understanding Implicit Bias: What Educators Should Know, Am. Educ., Winter 2015–2016, at 29, 29, http://www.aft.org/sites/default/files/ae_winter2015staats.pdf (“As a profession, teaching is full of well-intentioned individuals deeply committed to seeing all children succeed.”); cf. L. Song Richardson, Police Efficiency and the Fourth Amendment, 87 Ind. L.J. 1143, 1148 (2012) (“The typical arguments that the disproportionate policing of Blacks can be explained either by conscious racial bias on the part of the police or by the assumption that Blacks engage in more ambiguously criminal behavior does not withstand scrutiny.”). No doubt several factors contribute to harsh and poor treatment of racial minorities in schools, 293See, e.g., Daniel J. Losen et al., Disturbing Inequities: Exploring the Relationship Between Racial Disparities in Special Education Identification and Discipline, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 89, 91–92 (Daniel J. Losen ed., 2015) (explaining that minority students are more likely to have inexperienced teachers). a topic that I will not fully discuss here. Nevertheless, many researchers conclude that one of the causes of racial disparities in public education is the implicit (or unconscious) racial biases of teachers and school officials. 294See, e.g., Advancement Project, Power in Partnerships: Building Connections at the Intersection of Racial Justice and LGBTQ Movements to End the School-to-Prison Pipeline 5 (2015), http://b.3cdn.net/advancement/85066c4a18d249e72b_r23m68j37.pdf (“Implicit bias also plays a role in funneling Black, Brown, and LGBTQ students into the school-to-prison pipeline.”); Derek W. Black, Education Law: Equality, Fairness, and Reform 147 (2013) (“[T]oday racial discrimination is more likely to be the result of subtle or unconscious biases, on which a state actor may not even realize it is acting.”); Johanna Wald, Can “De-Biasing” Strategies Help to Reduce Racial Disparities in School Discipline?: Summary of the Literature 1–3 (2014), http://www.indiana.edu/~atlantic/wp-content/uploads/2014/03/Implicit-Bias_031214.pdf (arguing that the empirical evidence suggests that implicit racial bias contributes to differential treatment of racial minorities in schools); Jamilia J. Blake, Bettie Ray Butler & Danielle Smith, Challenging Middle-Class Notions of Femininity: The Causes of Black Females’ Disproportionate Suspension Rates, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 75, 76 (Daniel J. Losen ed., 2015) (“Although a number of factors are believed to contribute to disproportionate disciplinary practices, racial/ethnic bias has been implicated most frequently.”); Pamela Fenning & Jennifer Rose, Overrepresentation of African American Students in Exclusionary Discipline: The Role of School Policy, 42 Urb. Educ. 536, 537 (2007) (arguing that students of color are targeted by teachers out of fear and anxiety of losing control of the classroom); Kent McIntosh et al., Education Not Incarceration: A Conceptual Model for Reducing Racial and Ethnic Disproportionality in School Discipline, 5 J. Applied Res. on Child. 6–7 (2014), http://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=1215&context=childrenatrisk (maintaining that conscious or unconscious bias is an important factor in the discipline gap); see also Nance, Over-Disciplining Students, supra note 1, at 1067–68; cf. Gary Blasi, Advocacy Against the Stereotype: Lessons from Cognitive Social Psychology, 49 UCLA L. Rev. 1241, 1276 (2002) (maintaining that Americans’ behavior is determined to some degree by unconscious racial biases); Jerry Kang, Trojan Horses of Race, 118 Harv. L. Rev. 1489, 1512–17 (2005) (discussing social cognition research that shows that most people hold implicit biases against racial minorities); Cynthia Lee, Making Race Salient: Trayvon Martin and Implicit Bias in a Not Yet Post-Racial Society, 91 N.C. L. Rev. 1555, 1570 (2013) (“Despite our largely egalitarian attitudes and beliefs, social science research over the past decade has shown that a majority of Americans are implicitly biased against Blacks.”); Jeffery J. Rachlinski et al., Does Unconscious Racial Bias Affect Trial Judges?, 84 Notre Dame L. Rev. 1195, 1196–97 (2009) (“Implicit bias . . . also appears to be an important source of racial disparities in the criminal justice system.”).

A. The Science of Implicit Bias

Our understanding of human cognitive processes has proliferated in recent years. 295See, e.g., Daniel Kahneman, Thinking, Fast and Slow (2011); Ziva Kunda, Social Cognition: Making Sense of People (1999); Nilanjana Dasgupta, Implicit Ingroup Favoritism, Outgroup Favoritism, and Their Behavioral Manifestations, 17 Soc. Just. Res. 143, 144–46 (2004) (describing the development of implicit bias theory); Jonathan St. B.T. Evans & Keith E. Stanovich, Dual-Process Theories of Higher Cognition: Advancing the Debate, 8 Persp. on Psych. Sci. 223 (2013); Matthew D. Lieberman, Reflexive and Reflective Judgment Processes: A Social Cognition Neuroscience Approach, in Social Judgments: Implicit and Explicit Processes 44 (Joseph P. Forgas, Kipling D. Williams & William Von Hippel eds., 2003); Eliot R. Smith & Jamie DeCoster, Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems, 4 Personality & Soc. Psych. Rev. 108 (2000). Over the past three decades, cognitive and social psychologists have constructed a solid knowledge base and tools to aid our understanding of the science of implicit social cognition. 296Dasgupta, supra note 295. Implicit social cognition science, on which the theory of implicit bias rests, examines mental processes that operate outside of our conscious awareness and volitional control. 297See id. at 144–46; see also Darren Lenard Hutchinson, “Continually Reminded of Their Inferior Position”: Social Dominance, Implicit Bias, Criminality, and Race, 46 Wash. U. J.L. & Pol’y 23, 35 (2014); Richardson, supra note 292, at 1146–47. Substantial empirical research demonstrates that human feelings, thoughts, perceptions, actions, decisionmaking, and behaviors are influenced by factors beyond conscious awareness or intention. 298See Kunda, supra note 295, at 266; Anthony G. Greenwald & Linda Hamilton Krieger, Implicit Bias: Scientific Foundations, 94 Calif. L. Rev. 945, 946 (2006); Hutchinson, supra note 297, at 35; Kristin A. Lane, Jerry Kang & Mahzarin R. Banaji, Implicit Social Cognition and Law, 3 Ann. Rev. L. Soc. Sci. 427, 428 (2007); Richardson, supra note 292, at 1146–47. In fact, some have described the assertion that individuals have conscious control over all of their judgments and actions as “naïve.” 299See Greenwald & Krieger, supra note 298, at 946; Richardson, supra note 292, at 1147.

Nobel Prize winner Daniel Kahneman established a widely accepted framework for understanding human cognition. 300Staats, supra note 292, at 29; see also Kahneman, supra note 295. Kahneman divided human cognition processing systems into two processes: System 1 and System 2. 301See Kahneman, supra note 295, at 20–21. Other scientists and researchers also have described the dual system of information processing, see, e.g., Evans & Stanovich, supra note 295, and some of them have referred to the dual system of information processing using other terms, see, e.g., Lieberman, supra note 295 at 46–47 (describing reflexive processes and reflective processes). However, not all dual process theories are alike. See Evan & Stanovich, supra note 295, at 226–27. Further, scientists are still researching whether there are indeed two systems, more than two systems, or simply one system with multiple processes. See Pamela Casey, Kevin Burke & Steve Leben, Minding the Court: Enhancing the Decision-Making Process 5 n.6 (2012). System 2 is conscious processing. 302Kahneman, supra note 295, at 21. It requires substantial working memory and is deliberate, reflective, rule-based, slow, controlled, and related to cognitive ability. 303See id. (“System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.”); Evans & Stanovich, supra note 295, at 223–25; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365. System 1 processing, on the other hand, is contextualized, associative, automatic, and quick. 304See Kahneman, supra note 295, at 20 (“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.”); Evans & Stanovich, supra note 295, at 223–25; McIntosh et al., supra note 294, at 5; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365–66. It operates mostly outside of a person’s conscious awareness and is independent of cognitive ability. 305See Kahneman, supra note 295, at 20; Evans & Stanovich, supra note 295, at 223–25; McIntosh et al., supra note 294, at 5; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365–66.

System 1 processing is valuable because it helps a person process information more quickly and function more efficiently in a fast-paced, complex world without expending valuable mental resources. 306Casey, Burke & Leben, supra note 301, at 5–6; Kang, supra note 294, at 1499; McIntosh et al., supra note 294, at 5; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 366. System 1 processing functions through the creation of schemas. 307See Kang, supra note 294, at 1498–99. Schemas are “cognitive structure[s] that represent[] knowledge about a concept or type of stimulus, including its attributes and the relations among those attributes.” 308Id. (quoting Susan T. Fiske & Shelley E. Taylor, Social Cognition (2d ed. 1991)). Essentially, schemas are knowledge templates that help us organize objects and information into broader categories. 309See Jerry Kang, Implicit Bias: A Primer for Courts 1 (2009). For example, if one sees an object with four legs, a back, and a horizontal plane, one immediately and intuitively classifies that object into the category of chairs, and one understands how to use that object automatically and nearly instantaneously without expending valuable mental resources. 310See Kang, supra note 294, at 1498–99.

Not only do humans categorize objects to make sense of their experiences and cope with the inundation of information they confront each day, but humans also categorize people. 311See id. at 1499; Richardson, supra note 285, at 2042. Such categorizations may occur along a number of identities that one perceives in another person, such as gender, age, disability status, and race. 312See Staats, supra note 292, at 32. As with the categorization of objects, the categorization of people occurs within System 1 processing; thus, that categorization is largely automatic, independent of cognitive ability, and operates without conscious awareness. 313Susan T. Fiske & Steven L. Neuberg, A Continuum of Impression Formation, from Category-Based to Individuating Processes: Influences of Information and Motivation on Attention and Interpretation, 23 Advances Experimental Soc. Psychol. 1, 23–24 (1990); Richardson, supra note 285, at 2042.

Implicit racial bias theory posits that implicit racial categorization that occurs automatically, unintentionally, and unconsciously, while helping us to sort and comprehend large amounts of information, also triggers implicit attitudes and implicit stereotypes. 314See Elise C. Boddie, Racial Territoriality, 58 UCLA L. Rev. 401, 439–40 (2010); Greenwald & Krieger, supra note 298, at 948–50; Richardson, supra note 292, at 1147. Attitudes are “association[s] between a concept (such as a social group) and a way of thinking or feeling, which can be positive or negative.” 315Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364; Jerry Kang et al., Implicit Bias in the Courtroom, 59 UCLA L. Rev. 1124, 1128 (2012); Nance, Over-Disciplining Students, supra note 1, at 1068–69. A person develops attitudes from past experiences “that inform and shape actions and preferences prospectively.” 316Hutchinson, supra note 297, at 35; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364; Nance, Over-Disciplining Students, supra note 1, at 1069. Stereotypes are associations between concepts (such as a social group) and a trait. 317Kang et al., supra note 315, at 1128; see also Greenwald & Krieger, supra note 298, at 949; Hutchinson, supra note 297, at 36; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364; Nance, Over-Disciplining Students, supra note 1, at 1069; Richardson, supra note 292, at 1147. Attitudes and stereotypes are related, but distinct. 318Kang et al., supra note 315, at 1128–29; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364. One can have a positive attitude toward African-Americans but still associate them with weapons. 319Kang et al., supra note 315, at 1129; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364. Conversely, one may associate Asian-Americans with high achievement in sciences, music, and mathematics but still feel poorly towards this group. 320Supra note 319.

B. The Implicit Association Test

How does one measure implicit bias? Cognitive psychologists have developed sophisticated techniques for measuring implicit biases. The most established measure is the Implicit Association Test, or IAT. 321See Anthony G. Greenwald et al., Understanding and Using Implicit Association Test: III. Meta-Analysis of Predictive Value, 97 J. Personality & Soc. Psychol. 17, 18–19 (2009) (discussing the multitude of studies that have confirmed the validity of the IAT); Hutchinson, supra note 297, at 38–39; Kang, supra note 294, at 1509 (“The Implicit Association Test (IAT) has become the state-of-the-art measurement tool.”); Kristen A. Lane et al., Understanding and Using the Implicit Association Test: IV: What We Know (So Far) About the Method, in Implicit Measures of Attitudes 59, 65 (Bernd Wittenbrink & Norbert Schwarz eds., 2007) (discussing how the IAT test has been employed in social cognition studies, clinical studies, health studies, marketing studies, legal studies, and to measures attitudes toward death, nature, celebrities, foods, cities, geography, public opinion issues, and politics); Rachlinski et al., supra note 294, at 1198. However, after reviewing the literature discussing the predictive value of the IAT, Marianne Bertrand and Esther Duflo caution that the IAT should not “be considered a ‘magic bullet,’ suitable to replace any other measure of discrimination.” Marianne Bertrand & Esther Duflo, Field Experiments on Discrimination 30–34 (Nat’l Bureau of Econ. Research, Working Paper No. 22014, 2016). Nevertheless, they conclude that the IAT “can be an extremely useful intermediate variable[] to understand the mechanisms beyond a result or potentially, if collected beforehand, a covariate of interest.” Id. at 34 (citation omitted). Created by Anthony Greenwald and developed by his research team, which included Mahzarin Banaji and Brian Nosek, 322See Mahzarin R. Banaji & Anthony G. Greenwald, Blind Spot: Hidden Biases of Good People 32–52 (2013); Rachlinski et al., supra note 294, at 1198. the IAT seeks to measure the connectedness, or valence, of concepts that underlie attitudes and stereotypes. 323See Banaji & Greenwald, supra note 322, at 39; Greenwald & Krieger, supra note 298, at 952. The most widely used IAT, the Race IAT, measures implicit bias towards African-Americans. 324Greenwald & Krieger, supra note 298, at 952. Other IAT tests measure biases with respect to Native Americans, age, disability, sexuality, gender, weight, Asians, skin-tone, and Arab-Muslims, among others. See Project Implicit: Take a Test, Harv. Univ., https://implicit.harvard.edu/implicit/selectatest.html (last visited Feb. 9, 2016). The Race IAT asks participants to perform a series of tasks. First, it asks participants to sort African-American faces and white faces by pressing computer keys on the left side and right side of the keyboard as they appear on the computer screen. 325Project Implicit: Take a Test, supra note 324. Second, the Race IAT asks participants to distinguish between pleasant and unpleasant words, again by pressing keys on the right side and left side of the keyboard. 326Id. The next two tasks are in random order and involve faces of African-Americans and whites, accompanied by pleasant and non-pleasant words. 327Id. In one of these tasks, the Race IAT asks participants to press one key when they view an African-American face or a pleasant word, and another key when they view a white face or an unpleasant word. 328Id. In the next task, the Race IAT requests participants to press one key when they view an African-American face and an unpleasant word, and another key when they view a white face or a pleasant word. 329Id. The implicit attitude measure is based on the comparative speed and accuracy of completing these tasks. 330Id.; see also Banaji & Greenwald, supra note 322, at 42.

Two important findings have emerged from the IAT in the last decade. 331Banaji & Greenwald, supra note 322, at 47. First, based on the millions of individuals who have taken the Race IAT, 332See Project Implicit: Take a Test, supra note 324. almost 75% of the test takers reveal an implicit preference for whites. 333Banaji & Greenwald, supra note 322, at 47; see also Brian A. Nosek, Mahzarin R. Banaji & Anthony G. Greenwald, Harvesting Implicit Group Attitudes and Beliefs from a Demonstration Web Site, 6 Group Dynamics 101, 105 (2002) (providing data that most test-takers of the Race IAT show an implicit white preference); Rachlinski et al., supra note 294, at 1199. That is, “most white Americans produce higher response latencies when faced with the stereotype-incongruent pairing (white/bad or black/good) than when faced with the stereotype-congruent pairing (white/good or black/bad).” 334Rachlinski et al., supra note 294, at 1199. Notably, African-Americans demonstrate different patterns of preference. Id. Overall, they exhibit a slight implicit preference for whites, but there is a wide variety in their responses, with some African-Americans expressing moderate to strong preferences for African-Americans, a preference rarely exhibited by whites. Id. at 1199–2000. Second, although some have levied criticism of the implicit bias theory and the IAT, 335See Hal R. Arkes & Philip E. Tetlock, Attributions of Implicit Prejudice, or “Would Jesse Jackson ‘Fail’ the Implicit Association Test?”, 15 Psychol. Inquiry 257 (2004); Gregory Mitchell & Philip E. Tetlock, Antidiscrimination Law and the Perils of Mindreading, 67 Ohio St. L.J. 1023 (2006); Frederick L. Oswald et al., Predicting Ethnic and Racial Discrimination: A Meta-Analysis of IAT Criterion Studies, 105 J. Personality & Soc. Psychol. 171 (2013); see also Bertrand & Duflo, supra note 321, at 32–33 (discussing studies questioning the predictive value of the IAT). empirical evidence shows that white preference measured by the Race IAT predicts discriminatory behavior even among persons who claim to hold egalitarian beliefs. 336Banaji & Greenwald, supra note 322, at 47. For example, the Race IAT predicted white preference behavior in contexts such as interviewing and hiring, 337See Allen R. McConnell & Jill M. Leibold, Relations Among the Implicit Association Test, Discriminatory Behavior, and Explicit Measures of Racial Attitudes, 37 J. Experimental Soc. Psychol. 435, 440–42 (2001) (finding that those that tested for stronger negative attitudes towards African-Americans on the IAT exhibited more negative social interactions with African-American interviewers); Jonathan C. Ziegert & Paul J. Hanges, Employment Discrimination: The Role of Implicit Attitudes, Motivation, and a Climate for Racial Bias, 90 J. Applied Psychol. 553, 560–61 (2005) (empirically finding that implicit racism as measured by the Race IAT predicted racially biased discriminatory actions). physicians’ health care decisions, 338See Alexander R. Green et al., Implicit Bias Among Physicians and Its Prediction of Thrombolysis Decisions for Black and White Patients, 22 J. Gen. Internal Med. 1231, 1235–37 (2007) (finding empirically that physicians’ self-reported explicit biases did not influence decisions for optimal medical treatment, but their implicit biases, as measured by the Race IAT, did influence those decisions). and perceiving facial emotion displayed by others. 339See Kurt Hugenberg & Galen V. Bodenhausen, Facing Prejudice: Implicit Prejudice and the Perception of Facial Threat, 14 Psychol. Sci. 640, 643 (2003) (finding empirically that participants with higher implicit biases as measured by the Race IAT “saw hostility as lingering longer and appearing more quickly on the faces of African Americans” compared to participants who had lower implicit biases). Anthony Greenwald and his colleagues conducted a meta-analysis of 122 research studies of implicit bias that included 184 independent samples and 14,900 research subjects. 340See Anthony Greenwald et al., supra note 321. They found substantial support for the predictive validity of the IAT. 341See Banaji & Greenwald, supra note 322, at 49 (“The meta-analysis answered the most important question about which we had been uncertain in the first several years of the IAT’s existence: It clearly showed that the Race IAT predicted racially discriminatory behavior.”). See generally Greenwald et al., supra note 321. Further, research has demonstrated that the results from the IAT test are not determined by participants’ handedness, the order in which participants perform the requested tasks, or another artifact of the experimental design. See Anthony G. Greenwald & Brian A. Nosek, Health of the Implicit Association Test at Age 3, 48 Zeitschrift für Experimentelle Psychologie 85, 87 (2001) (showing that handedness was not related to the Race IAT results); Anthony G. Greenwald, Brian A. Nosek & Mahzarin R. Banaji, Understanding and Using Implicit Association Test: I. An Improved Scoring Algorithm, 85 J. Personality & Soc. Psychol. 197, 209–11 (2003) (making adjustments to reduce order effect); Rachlinski et al., supra note 294, at 1200–01. But see Oswald et al., supra note 335, at 182–83 (finding that IATs were poor predictors of a wide range of discrimination measures except for brain activity). In short, “[t]he prevailing wisdom is that IAT scores reveal implicit or unconscious bias.” 342Rachlinski et al., supra note 294, at 1201 (footnotes omitted); see also Bertrand & Duflo, supra note 321, at 34 (discussing the utility of the IAT after reviewing the literature discussing the predictive value of the IAT).

C. Empirical Studies Implicating the Existence of Implicit Racial Bias

Critically, empirical research demonstrates that individuals often harbor implicit attitudes and beliefs about racial groups that are inconsistent with their conscious attitudes and beliefs. 343See Hutchinson, supra note 316, at 37; Rachlinski et al., supra note 294, at 1197; Richardson, supra note 285, at 2043. Thus, implicit attitudes and stereotypes can negatively influence a person’s judgment and decisionmaking in ways that the person is unaware of, unable to control, or would not explicitly endorse, even when the person consciously strives to be an egalitarian. 344Casey, Burke & Levin, supra note 301, at 10; Kang, supra note 294, at 1514; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365; Nance, Over-Disciplining Students, supra note 1, at 1069; Richardson, supra note 285, at 2043.

Several empirical studies have documented that many people implicitly associate African-Americans with aggression, criminality, violence, and danger. 345See Richardson, supra note 292, at 1147; Staats, supra note 292, at 31; see also Jennifer L. Eberhardt et al., Seeing Black: Race, Crime, and Visual Processing, J. Personality & Soc. Psychol. 876, 876 (2004) (“The stereotype of Black Americans as violent and criminal has been documented by social psychologists for almost 60 years.”); Katheryn Russell-Brown, Black Men and the Police: Making Implicit Bias Explicit, in Policing Black Men: Arrest, Prosecution and Prison (Angela J. Davis ed., forthcoming) (manuscript at 11) (“Overall, [the four studies] demonstrate that the sledge hammer of racial bias will have a disproportionate impact on those deemed ‘most’ Black.”); Sophie Trawalter et al., Attending to Threat: Race-Based Patterns of Selective Attention, 44 J. Experimental Soc. Psychol. 1322, 1326–27 (2008). For example, Keith Payne conducted experiments measuring the influence of racial cues on the perceptual identification of weapons. 346B. Keith Payne, Prejudice and Perception: The Role of Automatic and Controlled Processes in Misperceiving a Weapon, 81 J. Personality & Soc. Psychol. 181 (2001). In his first experiment, Payne discovered that participants identified guns faster when they were primed by seeing an African-American face rather than a white face. 347Id. at 185. He also found that participants identified tools faster when they were primed by seeing a white face rather than the face of an African-American. 348Id. In his second experiment, when the participants were under a timed deadline to increase the error rate, participants primed by seeing the face of an African-American were more likely to falsely identify a tool as a gun compared to participants primed by seeing a white face. 349Id. at 188.

In another gun study, Joshua Correll and his colleagues created a videogame where African-Americans and whites appeared in a series of backgrounds either holding a gun or holding a different object such as a cell phone, camera, aluminum can, or wallet. 350Joshua Correll et al., The Police Officer’s Dilemma: Using Ethnicity to Disambiguate Potentially Threatening Individuals, 83 J. Personality & Soc. Psychol. 1314, 1315 (2002). Almost all of the participants in the studies were white. 351Id. at 1315, 1318, 1321. The researchers discovered that participants fired at armed targets more quickly if the target was African-American rather than white, and decided not to shoot unarmed targets more quickly if the unarmed target was white rather than African-American. 352Id. at 1317. When the researchers imposed a time limit for participants to respond and participants received a financial incentive to respond correctly, the researchers found that when the target was unarmed, participants erroneously shot the target more often when the target was African-American rather than white. 353Id. at 1319. However, when the target was armed, participants erroneously decided not to shoot more often when the target was white rather than African-American. 354Id. The researchers also asked a series of questions to assess the participants’ (1) personal endorsement and belief of stereotypes of African-Americans, and (2) awareness of stereotypes generally present in American culture. 355Id. at 1321. They found that “shooter bias” 356The researchers define “shooter bias” as “faster responses to unarmed White than to unarmed African American targets, and to armed African American than armed White targets.” Id. at 1322. did not correlate with personally held stereotypes but was strongly related to the awareness of cultural stereotypes. 357Id. at 1322–24. Finally, these researchers recruited African-Americans as participants in the study. 358Id. at 1324. Interestingly, the results of the study did not change; that is, the researchers found equivalent levels of bias in African-American participants towards African-American targets. 359Id.

John Bargh and his colleagues conducted an experiment where they asked participants, none of whom were African-Americans, to work on a computerized visual task that was boring and tedious. 360John A. Bargh, Mark Chen & Lara Burrows, Automaticity of Social Behavior: Direct Effects of Trait Construct and Stereotype Activation on Action, 71 J. Personality & Soc. Psychol. 230, 238 (1996). Before each trial, the computer subliminally displayed a picture of an African-American male or white male face. 361Id. On the 130th trial, the computer alerted the participant that the system failed and the participant would have to repeat the experiment. 362Id. A hidden camera recorded the experiments, and third parties observed the participants’ reactions. 363Id. The third-party observers were unaware of whether the participants had subliminally viewed African-American or white faces. 364Id. at 239. The researchers discovered that participants who were primed with photographs of African-American faces behaved in a more hostile fashion upon learning that the system failed compared to participants primed with photographs of white faces. 365Id. Further, the researchers discovered that the participants’ hostility did not correlate with the participants’ stated, conscious attitudes towards race. 366Id.

Sandra Graham and Brian Lowery conducted experiments assessing police officers’ and juvenile probation officers’ reactions to stories about hypothetical youth who allegedly committed crimes. 367See Sandra Graham & Brian S. Lowery, Priming Unconscious Racial Stereotypes About Adolescent Offenders, 28 Law & Hum. Behav. 483, 487 (2004). Before experimenters asked any questions, participants were subliminally exposed to words on a screen relating to either African-Americans 368Those words included Harlem, homeboy, dreadlocks, basketball, minority, black, and afro, among others. Id. at 489 n.5. or words neutral to race and ethnicity. 369Those words included baby, enjoyment, heaven, kindness, summer, sunset, truth, devil, accident, funeral, horror, mosquito, stress, and toothache, among others. Id. The experimenters did not mention the race of the youth who allegedly committed a crime, and the causes of the crime were ambiguous. 370Id. at 487. The experimenters then asked the participants to make judgments about the hypothetical youth’s culpability, expected recidivism, deserved punishment, hostility, and maturity level. 371Id. at 487, 496. The experimenters found that police officers and juvenile probation officers primed by race were more likely to describe the hypothetical offender as culpable, deserving of punishment, and mature. 372Id. at 494. Importantly, police officers’ and juvenile probation officers’ consciously held beliefs and attitudes about race were not significantly related to their judgments about the hypothetical offender. 373Id. at 494, 497. The researchers concluded that “unconscious stereotypes can be activated in police and probation officers; [and] once activated, stereotypes influenced attributionally relevant judgments about offenders’ negative traits, culpability, likely recidivism, and deserved punishment.” 374Id. at 499.

In yet another study, Frank Gilliam and Shanto Iyengar conducted an experiment where one group of participants watched a news story featuring an alleged perpetrator who was an African-American male, and another group watched the same story except that the alleged perpetrator was a white male. 375Franklin D. Gilliam, Jr. & Shanto Iyengar, Prime Suspects: The Influence of Local Television News on the Viewing Public, 44 Am. J. Pol. Sci. 560, 563 (2000). The pictures of the perpetrators were equivalent in all respects except for skin color. 376Id. The experimenters discovered that when the alleged perpetrator was African-American, participants more strongly supported punitive crime policies. 377Id. at 567–68. The studies discussed here represent only a small sample of the robust research demonstrating the implicit racial biases that many Americans have towards African-Americans and other racial minorities. 378See, e.g., Birt L. Duncan, Differential Social Perception and Attribution of Intergroup Violence: Testing the Lower Limits of Stereotyping of Blacks, 34 J. Personality & Soc. Psychol. 590, 595 (1976) (finding that when, following a script, one white man shoved another white man, only 13% of viewers thought that this act was aggressive; but when both men were black, following the same script, that number rose to 69%); Eberhardt et al., supra note 345 (showing that participants who were subliminally primed with crime-relevant objects induced attentional biases towards African-American faces); Kurt Hugenberg & Galen V. Bodenhausen, Ambiguity in Social Categorization: The Role of Prejudice and Facial Affect in Race Categorization, 15 Psychol. Sci. 342 (2004) (demonstrating that implicit bias towards African-Americans, not explicit bias, was more strongly related with a tendency to categorize racially ambiguous faces of African-Americans as hostile).

D. Evidence of Implicit Racial Bias in School Settings and Its Effects

If researchers have observed implicit racial biases among police officers, 379See, e.g., Joshua Correll et al., Across the Thin Blue Line: Police Officers and Racial Bias in the Decision to Shoot, 92 J. Personality & Soc. Psychol. 1006, 1020–22 (2007) (finding empirically when the targets were African-Americans, police officers manifested “robust racial bias in the speed with which they made shoot/don’t-shoot decisions”). physicians, 380See Green et al., supra note 338, at 1235–37; see also Janice A. Sabin et al., Physicians’ Implicit and Explicit Attitudes About Race by MD Race, Ethnicity, and Gender, 20 J. Health Care for Poor & Underserved 896, 901 (2009) (demonstrating empirically that the medical doctors participating in the study exhibited an overall strong implicit preference for whites over African-Americans). judges, 381See Lee, supra note 294, at 1559; Rachlinski et al., supra note 294, at 1210 (finding empirically “strong white preference among the white judges”). and the general public, 382See Banaji & Greenwald, supra note 322, at 47. we should not be surprised to learn that implicit racial biases also influence educators’ perceptions, actions, decisionmaking, and behaviors. The few experiments that researchers have conducted on educators reveal the existence and effects of educators’ implicit racial biases on students.

For example, Linda van den Bergh and her colleagues conducted an experiment to examine the differences in academic achievement among minority and majority students in Holland. 383Linda van den Bergh et al., The Implicit Prejudiced Attitudes of Teachers: Relations to Teacher Expectations and the Ethnic Achievement Gap, 47 Am. Educ. Res. J. 497, 504 (2010). The ethnic minority students were of Turkish and Moroccan origin; the majority students were of Dutch origin. Id. They assessed whether those achievement gaps were associated with differential teacher expectations and explicit and implicit racial biases towards minority students. 384Id. Their experiment revealed that not only did teachers generally have lower academic expectations for minority students, but those expectations decreased even further for teachers with higher implicit racial biases. 385Id. at 512. They also discovered that teachers’ negative implicit racial biases towards minority students were associated with lower academic achievement for those student groups, and according to the researchers, the effects were substantial. 386Id. at 514, 518. Furthermore, consistent with research in other areas, they discovered that the implicit racial biases of the teachers were more strongly associated with teachers’ expectations and student achievement than their explicit biases. 387Id. at 519; see also Cheryl Staats, Kirwin Inst. for the Study of Race & Ethnicity, State of the Science: Implicit Bias Review 30–34 (2013); Clark McKown & Rhona S. Weinstein, Modeling the Role of Child Ethnicity and Gender in Children’s Differential Response to Teacher Expectations, 32 J. Applied Soc. Psychol. 159, 174–80 (2002) (finding that race predicts teacher expectancy effects that may exacerbate racial achievement gaps); Clark McKown & Rhona S. Weinstein, Teacher Expectations, Classroom Context, and the Achievement Gap, 46 J. Sch. Psychol. 235, 256 (2008) (showing empirically that teachers with high biases towards minority students experienced higher gaps in student achievement along racial lines than teachers with lower biases); Harriet R. Tenenbaum & Martin D. Ruck, Are Teachers’ Expectations Different for Racial Minority than for European American Students? A Meta-Analysis, 99 J. Educ. Psychol. 253, 271 (2007) (demonstrating that teachers have higher expectations for white students than for minority students, and that teacher expectancies may lead to differences in academic performances).

In another study, researchers Jason Okonofua and Jennifer Eberhardt conducted controlled experiments to assess how race affects teachers’ responses to student misbehavior. 388Jason A. Okonofua & Jennifer L. Eberhardt, Two Strikes: Race and the Disciplining of Young Students, 26 Psychol. Sci. 617, 617 (2015). The researchers displayed to teachers a fictitious record of a student who misbehaved twice—once for insubordination and the other for class disturbance. 389Id. at 618. The researchers counterbalanced the order in which they displayed the infractions across participants. Id. The researchers manipulated student race by using stereotypically African-American names (Deshawn or Darnell) or white names (Jake or Greg). 390Id. The researchers then asked several questions to examine the influence of race on teachers’ responses to the student’s minor infractions. 391Id. These questions targeted teachers’ feelings towards the severity of the student’s misbehavior, teachers’ irritation towards the student, the severity of the punishment, and how likely they were to label the student as a “troublemaker.” 392Id. The researchers found that the teachers were “significantly more troubled by the second infraction” when the student was African-American. 393Id. at 619. In addition, after the second infraction, teachers felt that the African-American student should be disciplined more harshly than the white student. 394Id. Moreover, after the second infraction, teachers were more likely to label an African-American student as a “troublemaker,” believe that the misbehavior was indicative of a negative pattern, and imagine suspending the student in the future. 395Id. at 621; see also Clifton A. Casteel, Teacher-Student Interactions and Race in Integrated Classrooms, 92 J. Educ. Res., Nov./Dec. 1998, at 115, 119 (finding empirically that African-American students had more negative interactions with white teachers than white students, and white students had more positive interactions with teachers than African-American students); cf. Justin D. Levinson, Forgotten Racial Equality: Implicit Bias, Decisionmaking, and Misremembering, 57 Duke L.J. 345, 398–406 (2007) (demonstrating empirically that participants remembered more facts “relating to the actor’s aggressive actions” when listening to a story about “Tyronne” than listening to the same story about “William”). This study is consistent with other empirical studies which, though not controlled, show a strong association between student race and the use of punitive disciplinary measures. 396See supra notes 271–80 and accompanying text.

E. Implicit Racial Bias and Racial Spaces

The implicit social cognition research discussed above provides substantial empirical support for the existence of implicit racial biases and their detrimental effects on minorities in a variety of contexts, including in school environments. While not the only factor causing racial disparities in education, implicit racial bias may explain, to some extent, the racial disparities that persist in school discipline, academic achievement, grade retention, placement in special education, and placement in other restrictive settings. 397See supra Part III. However, can implicit racial bias also help explain the disparities relating to the use of strict security measures, particularly when not all of the students at a school are students of color?

Robert Sampson and Stephen Raudenbush conducted an important empirical study that elucidates the connection between implicit racial biases and neighborhoods that can be applied to other racial spaces such as schools. 398See Robert J. Sampson & Stephen W. Raudenbush, Seeing Disorder: Neighborhood Stigma and the Social Construction of “Broken Windows”, 67 Soc. Psychol. Q. 319 (2004). Sampson and Raudenbush analyzed how individuals perceive disorder within and between neighborhoods, comparing those perceptions with “independent assessments of disorder that are reliable and ecologically valid.” 399Id. at 324. They hypothesized that the racial compositions of neighborhoods would skew individuals’ subjective perceptions of disorder in those neighborhoods. 400Id. at 322–24. Evaluating census data, police data recording violent crimes, data from personal interviews of neighborhood residents, and social observations of city streets, 401Id. at 324–27. the researchers discovered that neighborhoods’ racial compositions of African-American citizens and Latino citizens were more powerful predictors of neighborhood residents’ subjective perceptions of neighborhood disorder than careful, actual observations of neighborhood disorder. 402Id. at 330, 336. Consistent with prior studies examining implicit biases, they found that African-Americans were “no less likely than whites to be influenced by racial composition in predicting disorder.” 403Id. at 336. Additionally, the researchers replicated these findings on an independent dataset from community leaders who worked in the sample communities, but did not live in those communities. 404Id.

Sampson and Raudenbush’s study was consistent with findings from a prior study conducted by Lincoln Quillian and Devah Pager. 405See Lincoln Quillian & Devah Pager, Black Neighbors, Higher Crime? The Role of Racial Stereotypes in Evaluations of Neighborhood Crime, 107 Am. J. Soc. 717 (2001). Examining survey data, census data, and police crime statistics from Chicago, Seattle, and Baltimore, Quillian and Pager found that a neighborhood’s concentration of young, male African-Americans was “one of the best predictors of the perceived severity of neighborhood crime,” even after controlling for other variables such as crime rates, victimization rates, and neighborhood deterioration factors. 406Id. at 736–37, 741, 743, 747. They concluded that their results “suggest that the strong mental association between race and crime has a powerful influence on perceptions of neighborhood crime levels, beyond any actual association between race and crime.” 407Id. at 748.

In another relevant study, Joshua Correll and his colleagues examined police officers’ tendency to shoot or not shoot African-American and white targets. 408Correll et al., supra note 379. The researchers discovered that implicit racial biases increased among police officers serving in urban environments with higher proportions of African-American residents. 409Id. at 1015. Thus, as Song Richardson observed, “officers whose primary experience is based on proactive policing in urban, poor, and majority-black neighborhoods may have higher levels of implicit bias which can result in them being less accurate than officers whose primary experience consists of work in other neighborhoods.” 410Richardson, supra note 292, at 1160; cf. Servoss, supra note 128, at 18 (finding that teachers in high security schools rated African-American students as more disruptive relative to their white peers and suggesting that teachers’ biases may be exacerbated against African-American students in higher security environments).

All of these studies suggest that not only can the race of an individual person trigger implicit biases, but so can spaces—such as neighborhoods and schools—where a significant number of minorities are present. Accordingly, even if not all of the students in the school are students of color, working in a school serving a high concentration of minority students still may unconsciously affect school officials’ perceptions, actions, behaviors, and decisionmaking regarding how to create an orderly learning environment, especially when school officials may unconsciously associate minority students with danger, crime, disorder, and violence. 411See Servoss & Finn, supra note 141, at 64 (explaining that as the proportion of minorities in a school rises, school officials are more prone to rely on punitive discipline methods to combat crime-related threats and to maintain dominance).

V. Proposals for Reform

The use of intense, coercive surveillance methods, especially when applied disproportionately to students of color, harms students’ interests, delegitimizes the educational process, perpetuates racial inequalities, weakens trust in government institutions and processes, skews minorities’ perceptions of their standing in our society, and sends harmful messages to members of all races that students attending majority-white schools enjoy greater privileges and have superior privacy rights. 412See supra Part II. Furthermore, while one might try to justify these disparities on the basis that majority-minority schools often face greater safety concerns, the empirical evidence demonstrates that these racial disparities exist even after taking into account factors such as school crime, school disorder, and neighborhood crime, suggesting that other factors—such as implicit racial bias—may also influence school officials’ decisionmaking to some degree. 413See supra Part III. The question now is how to address this problem.

Elsewhere, I have argued that school-led reform is the most effective means of addressing over-reliance on strict security measures and its disproportionate use on students of color. 414See Nance, Students, Security, and Race, supra note 1, at 48–55; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 345–60. Schools should apply alternative, evidence-based methods to promote safe learning environments, such as restorative justice, Schoolwide Positive Behavioral Interventions and Supports, social and emotional learning, and training to improve the teaching and classroom management skills of teachers. 415See supra note 414. I have also urged the U.S. Department of Education’s Office for Civil Rights to play a more active role in reducing racial disparities relating to the use of strict security measures. 416See Nance, Students, Security, and Race, supra note 1, at 55. Further, I have argued that state and federal agencies should stop providing money for strict security measures and instead help fund and establish incentives for schools to employ these alternative measures. 417See id. Importantly, the U.S. Department of Education’s Office for Civil Rights recently recommended that schools use these approaches to create safe school climates. See Guiding Principles, supra note 153, at 5–7; Dear Colleague Letter, supra note 4, at app. 2. I broaden this recommendation now, urging governments to drastically reduce expenditures associated with the criminal justice system, and instead invest those funds in public education and the programs and initiatives described above. Disturbingly, according to data recently released by the U.S. Department of Education, expenditures on public Pre-K–12 education increased by 107% from 1979–1980 to 2012–2013, but expenditures on state and local correction increased by 324% over that same time period. 418See U.S. Dep’t of Educ., State and Local Expenditures on Corrections and Education: A Brief from the U.S. Department of education, Policy and Program Studies Service 1 (2016). In addition to improving the quality of life for thousands of students, multiple empirical studies demonstrate the economic savings and benefits our nation could achieve by investing more in public education. See id. at 2–3.

In addition, there are concrete steps that the federal government and state governments should take to address the ill effects of implicit racial bias on school security decisions. 419Courts also have a role to play. In my prior work, I proposed a reformulated legal framework to evaluate students’ Fourth Amendment rights in schools. See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83–84. In an upcoming article, I will propose another legal framework for courts to evaluate the constitutionality of coercive surveillance methods, one that will address the effects of implicit racial bias and help rectify the disproportionate application of strict security measures on students of color. Importantly, not only will these recommendations help address the disproportionate use of strict security measures on students of color, but they will also help address racial disparities in other areas of public education, such as student discipline, student placement, and academic achievement. 420See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 362–71.

A. Increased Support from the Federal Government and State Governments to Address Educators’ Implicit Racial Biases

First, as part of the Elementary and Secondary Education Act, the federal government should require, in exchange for federal education funds, that states enact laws mandating all school personnel receive implicit bias training, and all teacher certification programs include such training. 421See Nance, Over-Disciplining Students, supra note 1, at 1072. It is imperative that educators understand the concepts associated with implicit bias, its pernicious effects on decisionmaking, and what they can do to neutralize its influence. Indeed, even though implicit racial biases are deeply rooted in our subconscious minds, they are still malleable and their effects can be neutralized. 422See Staats, supra note 387, at 53; Irene V. Blair, The Malleability of Automatic Stereotypes and Prejudice, 6 J. Personality & Soc. Psychol. Rev. 242, 247–48 (2002); Nilanjana Dasgupta & Anthony G. Greenwald, On the Malleability of Automatic Attitudes: Combating Automatic Prejudice with Images of Admired Disliked Individuals, 81 J. Personality & Soc. Psychol. 800, 808 (2001); Patricia G. Devine, Stereotypes and Prejudice: Their Automatic and Controlled Components, 56 J. Personality & Soc. Psychol. 5, 15–16 (1989); Erik J. Girvan, On Using the Psychological Science of Implicit Bias to Advance Anti-Discrimination Law, 26 Geo. Mason U. C.R.L.J. 1, 78 (2015); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 367. If the federal government does not enact a law requiring states to provide this training, state legislatures should enact such legislation on their own. 423See Nance, Over-Disciplining Students, supra note 1, at 1072.

Notably, the U.S. Department of Justice recently announced that it will require all of its attorneys and law enforcement agents to receive implicit bias training. 424See Julia Edwards, Justice Dept. Mandates ‘Implicit Bias’ Training for Agents, Lawyers, Reuters (June 27, 2016, 9:14 PM), www.reuters.com/articles/us-usa-justice-bias-exclusive-idUSKCN0ZD251. The Justice Department made this decision after observing success with such training programs in local jurisdictions. 425Id. Likewise, police departments throughout the country, including in major cities such as Baltimore, New York City, Seattle, New Orleans, and Los Angeles, require their employees to participate in implicit bias training. 426Id.; see also Russell-Brown, supra note 345. In addition, the American Bar Association recently launched an initiative to “combat implicit bias in the judicial system” by providing resources to “bar leaders, legal educators, judges, other justice system stake-holders, and community leaders in general” so they can “present programming on implicit bias in their own communities.” 427Implicit Bias Initiative, Am. Bar Ass’n, http://www.americanbar.org/groups/litigation/initiatives/task-force-implicit-bias.html (last visited July 14, 2016).

Furthermore, it is important that these laws require educators to receive implicit racial bias training regularly, perhaps at least annually. Field-tested strategies and interventions to debias individuals are still in their early stages. 428Girvan, supra note 422, at 78; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 367. As the science of debiasing becomes more understood and sophisticated, that new knowledge must be shared with educators. In addition, research suggests that positive changes associated with debiasing strategies are elastic, and biases may return to their earlier configurations if participants do not reapply strategies and techniques. 429See Banaji & Greenwald, supra note 322, at 152. Thus, regular training may assist educators to more effectively neutralize their biases over longer periods of time. 430Cheryl Staats, Implicit Bias, Intergroup Contact, and Debiasing: Considering Neighborhood Dynamics, N.Y.U. Furman Ctr. (Aug. 2014), http://furmancenter.org/research/iri/essay/implicit-bias-intergroup-contact-and-debiasing-considering-neighborhood-dyn (explaining that “[d]ebiasing requires consistent effort to modify existing mental associations”).

Second, the federal government and state governments should provide financial assistance to impoverished school districts to ensure that these school districts can provide adequate implicit bias training to their employees. Implicit bias training requires resources that many cash-strapped school districts lack. 431See Nance, Persisting Inequalities, supra note 3 (discussing resource inequalities among schools). Financial assistance to poorer school districts is particularly important because poorer school districts often serve higher concentrations of minority students. 432See supra note 103; see also Natasha Ushomirsky & David Williams, Funding Gaps 2015: Too Many States Still Spend Less on Educating Students Who Need the Most 1 (2015) (finding that school districts serving the highest concentrations of minority students on average spend $2000 less per student than school districts serving the highest concentrations of white students).

Third, the federal government and state governments should fund more research to advance the science of implicit racial bias, particularly as it applies to educators. 433See Nance, Over-Disciplining Students, supra note 1, at 1072. As stated above, although researchers have a reasonable understanding of the concept of implicit bias, the science behind its causes, effects, and how to neutralize its negative effects is less advanced. Indeed, it is critical to increase our understanding of implicit racial bias and how to neutralize its effects because it appears that implicit racial bias imbues educators’ decisions and actions in several areas outside of school security, including student discipline, student achievement, and student placement. 434See supra Part IV.D. Furthermore, as the U.S. Department of Education and state departments of education do with respect to other areas of education, these government agencies should take a much more active role in analyzing and disseminating information about implicit bias, particularly with respect to best practices regarding how to neutralize its effects, as our understanding of this complex science increases. 435Nance, Over-Disciplining Students, supra note 1, at 1073.

B. Require Schools to Publicly Report Their Security Practices

Although the Civil Rights Data Collection provides much needed information to the public regarding many areas of K–12 education, the federal government and state governments currently do not require all schools to publicly report information about their security practices. 436The SSOCS databases provide school security information only on a sample of public schools, see supra Part III, and much of that information, including the identity of the schools, is not publicly available. Accordingly, I urge the federal government and state governments to require each school to collect and publicly report detailed information regarding all security practices it employs, how often its school officials search individual students (or directs those searches), how often its school officials conduct (or direct) random, suspicionless searches on groups of students, and the reasons for these searches. 437Cf. Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 361 (urging governments to require schools to report more detailed data with respect to their disciplinary practices). I also urge the federal government and state governments to require each school to disclose the alternative measures it employs to promote a safe learning climate. This requirement may help motivate school officials to carefully consider why they rely on strict security measures (if they do so), which can help them confront their implicit racial biases. It may also help motivate school officials to rely on concrete data to make security decisions and perhaps more carefully consider whether they should implement alternative measures to create safer environments. Relatedly, the federal government and state governments should also consider holding schools accountable for relying too heavily on strict security measures and for racial disparities relating to their use. This can be done by incorporating these data into broader accountability rubrics used to evaluate schools and districts. 438Cf. id. (urging federal and state lawmakers to incorporate disciplinary data into their accountability rubrics); Daniel J. Losen, Conclusion, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 241, 248 (Daniel J. Losen ed., 2015) (“Ultimately, until discipline data are incorporated into the broader accountability rubrics used by states to evaluate schools and districts, it is unlikely we will witness comprehensive and lasting improvements in the area of school discipline.”).

Finally, I urge the federal government and state governments to make all of the information they gather on school security publicly available to help keep school officials accountable for their decisions. 439Cf. Daniel J. Losen, Directions for Broad Policy Change, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 15, 15 (Daniel J. Losen ed., 2015); Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 362 (urging lawmakers to make public all detailed information regarding school discipline to hold schools publicly accountable for their disciplinary decisions). Public data will provide parents with information they need to make informed decisions regarding where to send their children to school and for whom they should vote to serve on school boards and in other public offices. 440Cf. Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 362. Data will also provide parents with information they need to demand changes if necessary. 441Cf. id. And, critically, by making this information public, civil rights activists, lawmakers, lawyers, and others will have access to the information they need to push for reforms in this area. 442Cf. id. Indeed, since the U.S. Department of Education’s Office for Civil Rights has shined a powerful light on school districts’ exclusionary student discipline practices through its Civil Rights Data Collection, we have witnessed a decrease in the number of out-of-school suspensions nationwide. 443See Press Release, U.S. Dep’t of Educ., Persistent Disparities Found Through Comprehensive Civil Rights Survey Underscore Need for Continued Focus on Equity, King Says (June 7, 2016), http://www.ed.gov/news/press-releases/persistent-disparities-found-through-comprehensive-civil-rights-survey-underscore-need-continued-focus-equity-king-says (reporting that the total number of out-of-school suspensions decreased by 20% since the 2011–2012 school year). Importantly, however, the U.S. Department of Education also observed that the new data suggest that student discipline still “occurs in high numbers and disparities remain significant.” Id.

Conclusion

Intense surveillance practices, while uncommon just a few decades ago, are becoming more commonplace in schools. This trend is troubling because such practices can create hostile learning climates that generate inferior learning opportunities for youth. These intense measures also may end up pushing more students out of school and putting them on a pathway to prison. Furthermore, as empirical studies confirm, including an original empirical analysis of restricted data recently released by the U.S. Department of Education after the shootings at Sandy Hook Elementary School, not all schools rely on these strict measures. Rather, the data suggest that schools serving primarily students of color are more likely to implement more intense surveillance measures than other schools, which may further exacerbate existing inequalities between minority and white students. Importantly, empirical data and scientific studies also suggest that the implicit racial biases of school officials may influence school officials’ security decisions to some degree.

Educators can and should lead efforts to reform the disparate application of strict security measures on minority students and address educators’ implicit racial biases. Nevertheless, the federal government and state governments must take a much more active role to help school officials and teachers understand the concept of implicit bias, its unconscious influence on their decisionmaking, and how to neutralize its negative effects. Not only will this increased understanding help address the disproportionate application of strict security measures on students of color, but it will also help address racial disparities in other areas of public education. Such efforts will help move us closer to developing better learning environments for students of all races. Our youth deserve nothing less.

Footnotes

Associate Professor of Law, University of Florida Levin College of Law. J.D., University of Pennsylvania Law School; Ph.D., M.A., Educational Administration, The Ohio State University. I would like to thank the following individuals for the helpful feedback and assistance they provided for this Article: Nancy Dowd, Barry Feld, Michael Heise, Darren Hutchinson, Julie Nance, Sharon Rush, Katheryn Russell-Brown, Chris Slobogin, Greg Stein, John Stinneford, Michael Allan Wolf, and David Wolitz. I also appreciate the helpful comments provided by faculty members and conference participants during presentations of earlier versions of this work at the University of Tennessee College of Law and the Present and Future of Civil Rights Movements Conference at Duke Law School. Finally, I am also grateful for the excellent research assistance I received from Samanta Franchim, Anthony Kakoyannis, and Laura Liles.

1This Article builds upon my prior works on student surveillance, school security, the “school-to-prison pipeline,” racial inequalities, and implicit racial bias, particularly Jason P. Nance, Dismantling the School-to-Prison Pipeline: Tools for Change, 48 Ariz. St. L.J. 313 (2016) [hereinafter Nance, Dismantling the School-to-Prison Pipeline]; Jason P. Nance, Over-Disciplining Students, Racial Bias, and the School-to-Prison Pipeline, 50 U. Rich. L. Rev. 1063 (2016) [hereinafter Nance, Over-Disciplining Students]; Jason P. Nance, Random, Suspicionless Searches of Students’ Belongings: A Legal, Empirical, and Normative Analysis, 84 U. Colo. L. Rev. 367 (2013) [hereinafter Nance, Random, Suspicionless Searches]; Jason P. Nance, School Surveillance and the Fourth Amendment, 2014 Wis. L. Rev. 79 (2014) [hereinafter Nance, School Surveillance and the Fourth Amendment]; Jason P. Nance, Students, Police, and the School-to-Prison Pipeline, 93 Wash. U. L. Rev. 919 (2016); and Jason P. Nance, Students, Security, and Race, 63 Emory L.J. 1 (2013). Accordingly, to fully understand the significance of the theories, empirical analyses, and proposals for reform I present here, I draw upon, summarize, and highlight in this Article certain material discussed in my prior works for the reader’s convenience.

2347 U.S. 483 (1954).

3See, e.g., Gary Orfield & Chungmei Lee, Racial Transformation and the Changing Nature of Segregation 29–31 (2006) (describing inequalities in our public education system, especially with respect to minority students living in concentrated poverty); Robert D. Putnam, Our Kids: The American Dream in Crisis 135–90 (2015) (describing the inequalities and disparate opportunities that exist in our educational system); Linda Darling-Hammond, Inequality and School Resources: What It Will Take to Close the Opportunity Gap, in Closing the Opportunity Gap: What America Must Do to Give Every Child an Even Chance 77, 77–97 (Prudence L. Carter & Kevin G. Welner eds., 2013) (describing the inequalities present in our education system); Jason P. Nance, Persisting Inequalities in Educational Resources and Results: A Call for Reform, in The Road to Progress: The Case for a U.S. Education Amendment (Charles J. Ogletree, Jr. & Kimberly Jenkins Robinson eds., forthcoming) [hereinafter Nance, Persisting Inequalities] (describing the inequalities that persist in our public education system, especially with respect to low-income minority students); Kimberly Jade Norwood, Blackthink’s™ Acting White Stigma in Education and How It Fosters Academic Paralysis in Black Youth, 50 How. L.J. 711, 727–28 (2007) (describing many of the schools that black youth attend as “grossly underfunded”); Gary Orfield, The Growth of Segregation: African Americans, Latinos, and Unequal Education, in Dismantling Desegregation: The Quiet Reversal of Brown v. Board of Education 53, 69 (Gary Orfield & Susan E. Eaton eds., 1996) (“[D]isadvantaged students face more barriers and receive less reinforcement to succeed in school.”); Meredith Phillips & Tiffani Chin, School Inequality: What Do We Know?, in Social Inequality 467, 484–85 (Kathryn Neckerman ed., 2004) (describing class size inequalities); Press Release, U.S. Dep’t of Educ., New Data from U.S. Department of Education Highlights Educational Inequalities Around Teacher Experience, Discipline and High School Rigor (Mar. 6, 2012), http://www.ed.gov/news/press-releases/new-data-us-department-education-highlights-educational-inequities-around-teache (describing the inequalities that exist for minorities in our public education system); see generally Jonathan Kozol, Savage Inequalities: Children in America’s Schools (1991) (examining the racial segregation and inequality persisting in many schools and ways to resolve the problem based on children’s desires).

4See Letter from Catherine E. Lhamon, Assistant Sec’y, Office for Civil Rights, U.S. Dep’t of Educ., & Jocelyn Samuels, Acting Assistant Attorney Gen., Civil Rights Div., U.S. Dep’t of Justice, to Colleague, Dear Colleague Letter on the Nondiscriminatory Administration of School Discipline 3–5 (Jan. 8, 2014) [hereinafter Dear Colleague Letter], http://www2.ed.gov/about/offices/list/ocr/letters/colleague-201401-title-vi.pdf; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 331–36; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 957.

5See U.S. Gov’t Accountability Office, GAO-16-345, K–12 Education: Better Use of Information Could Help Agencies Identify Disparities and Address Racial Discrimination 21–22 (2016); Office of Civil Rights, U.S. Dep’t of Educ., 2013–2014 Civil Rights Data Collection: A First Look 6–7 (2016); Office of Civil Rights, U.S. Dep’t of Educ., Protecting Civil Rights, Advancing Equity 16–17 (2015); Putnam, supra note 3, at 174–83; Jennifer King Rice, Investing in Equal Opportunity: What Would It Take to Build the Balance Wheel? 8–9 (2015); Darling-Hammond, supra note 3, at 84–91; Orfield, supra note 3, at 53, 67–71; Phillips & Chin, supra note 3, at 490–99.

6See Linda Darling-Hammond, The Flat World and Education: How America’s Commitment to Equity Will Determine Our Future 37 (2010); Darling-Hammond, supra note 3, at 83; Nance, Persisting Inequalities, supra note 3; see also Gary Orfield et al., Brown at 60: Great Progress, a Long Retreat and an Uncertain Future 5 (2014) (documenting the problem of “double segregation,” meaning that a large and growing percentage of schools serve high concentrations of students who are both poor and students of color); Halley Potter, Kimberly Quick & Elizabeth Davies, A New Wave of School Integration: Districts and Charters Pursuing Socioeconomic Diversity 3 (2016) (observing that more than one-third of African-American students and Hispanic students attend schools where more than 90% of the students are students of color).

7See Daryl Khan, Perps or Pupils? Safety Policy Creates Prison-like New York City Schools, Juv. Just. Info. Exchange (Sept. 20, 2012), http://jjie.org/york-story/93676/.

8Id.

9Id.

10Id.

11Id.

12Id.

13Id.

14Id.

15Id.

16Id.

17Ending the School-to-Prison Pipeline: Hearing Before the S. Comm. on the Judiciary Subcomm. on the Const., Civil Rights and Human Rights of the S. Comm. on the Judiciary, 112th Cong. 1 (2012) (testimony of Edward Ward, Blocks Together, Dignity in Schools Campaign), https://www.judiciary.senate.gov/imo/media/doc/12-12-12WardTestimony.pdf.

18Id. at 1–2.

19Id. at 1, 3.

20Id. at 3–4.

21See Ellen Tuzzolo & Damon T. Hewitt, Rebuilding Inequity: The Re-Emergence of the School-to-Prison Pipeline in New Orleans, 90 High Sch. J., Dec. 2006–Jan. 2007, at 59, 66.

22Id.

23Id.

24Id.

25Id.

26See Aaron Kupchik, Things are Tough All Over: Race, Ethnicity, Class and School Discipline, 11 Punishment & Soc’y 291, 302–03 (2009) (observing that surveillance and policing in schools is pervasive, although more intense measures are more common in schools that serve poorer and minority students).

27See infra Part III.

28See infra Part III.

29See infra Part III.

30See infra Part IV. Of course, more empirical studies, including more observational studies, are needed to investigate the effect of implicit bias on school administrators’ decisionmaking generally, including their decisions to implement strict security measures.

31See Sarah Redfield, Can New Thinking Help Reverse the School-to-Prison Pipeline?, 5 A.B.A. Diversity Voice, Summer 2014, at 4, 4, http://www.americanbar.org/content/dam/aba/administrative/diversity/DVsummer2014.authcheckdam.pdf.

32See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 82–83. While poor school climates and dysfunctional learning environments often result from several factors, research suggests that over-reliance on intense surveillance methods and punitive methods generally hinders a school from developing a healthy school climate that is conducive to learning. See infra Part II.

33See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83.

34See Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 923.

35See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83; see also Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 923–24.

36See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83. “Zero-tolerance policies” are policies that mandate “the application of predetermined consequences, most often severe and punitive in nature, that are intended to be applied regardless of the gravity of behavior, mitigating circumstances, or situational context.” Am. Psychological Ass’n Zero Tolerance Task Force, Are Zero Tolerance Policies Effective in the Schools? An Evidentiary Review and Recommendations, 63 Am. Psychologist 852, 852 (2008). For a more complete discussion on zero-tolerance policies, see Derek W. Black, The Constitutional Limit of Zero Tolerance in Schools, 99 Minn. L. Rev. 823 (2015); and Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 932–34.

37See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94, 99–102; see also Barry C. Feld, T.L.O and Redding’s Unanswered (Misanswered) Fourth Amendment Questions: Few Rights and Fewer Remedies, 80 Miss. L.J. 847, 884–95 (2011) (explaining how the combination of certain policies and practices such as strict security measures and zero-tolerance policies have contributed to the school-to-prison pipeline); Jeremy D. Finn & Timothy J. Servoss, Security Measures and Discipline in American High Schools, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 44, 45 (Daniel J. Losen ed., 2015) (finding empirically that strict security measures are positively associated with increased suspension rates).

38See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 319–24.

39See Finn & Servoss, supra note 37, at 55 (empirically demonstrating that more African-American students are suspended in high security schools than white students).

40See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 91–102; Nance, Students, Security, and Race, supra note 1, at 7–16.

41See Nance, Random, Suspicionless Searches, supra note 1, at 376–87; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 91–102; Nance, Students, Security, and Race, supra note 1, at 7–16; James E. Ryan, The Supreme Court and Public Schools, 86 Va. L. Rev. 1335, 1415 (2000) (“[T]he Court’s decisions regarding student searches rest on the value-laden view that maintaining discipline is necessary to preserve the educational process of schools.”). Nevertheless, it is important to recognize that New Jersey v. T.L.O. held that students do retain their Fourth Amendment rights while at school. 469 U.S. 325, 332–33 (1985). Prior to T.L.O., some lower courts applied the in loco parentis doctrine, holding that students did not possess Fourth Amendment rights while at school because school administrators acted in the place of parents during school hours. See Nance, Random, Suspicionless Searches, supra note 1, at 377 n.38; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 128 n.284.

42See T.L.O., 469 U.S. at 340–42; Vernonia Sch. Dist. 47J v. Acton, 515 U.S. 646, 653–54 (1995); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 95–96; Nance, Students, Security, and Race, supra note 1, at 7–8.

43See Nance, Students, Security, and Race, supra note 1, at 8; see also infra Part II.

44See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 328–29; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 936–40; see also Catherine Y. Kim, Daniel J. Losen & Damon T. Hewitt, The School-to-Prison Pipeline: Structuring Legal Reform 118–20 (2010); Catherine Y. Kim, Policing School Discipline, 77 Brook. L. Rev. 861, 865–67 (2012).

45469 U.S. 325 (1985).

46Id. at 328.

47Id.

48Id.

49Id. at 329.

50Id. at 333–37.

51Id. at 332–33.

52Id. at 337–38.

53Id. at 339–40.

54Id. at 340.

55Id. at 340–41.

56Id. at 341(quoting Terry v. Ohio, 392 U.S. 1, 20 (1968)).

57515 U.S. 646 (1995).

58Id. at 650.

59Id. at 653.

60Id. at 652–53 (quoting Skinner v. Ry. Labor Execs.’ Assoc., 489 U.S. 602, 619 (1989)).

61Id. at 658–60.

62Id. at 656.

63Id. at 656–57. Moreover, the Court held that student athletes have even lower expectations of privacy because those students choose to participate in athletic programs, and they encounter certain conditions, such as locker rooms, where youth have less privacy. Id.

64Id. at 658.

65Id. at 661.

66Id. at 662–63.

67Id. at 664–65.

68536 U.S. 822 (2002).

69Id. at 825.

70Earls ex rel. Earls v. Bd. of Educ. of Tecumseh Pub. Sch. Dist., 242 F.3d 1264, 1278 (10th Cir. 2001).

71Earls, 536 U.S. at 824–25.

72Id. at 830–38.

73Id. at 834.

74Id. at 835.

75Id. at 834. Even more recently, the Court evaluated students’ Fourth Amendment rights again in Safford Unified School District Number 1 v. Redding, 557 U.S. 364 (2009). There, the Court decided whether school officials violated the Fourth Amendment when they performed a strip search on a thirteen-year-old female student attempting to find unauthorized prescription and over-the-counter drugs. Id. at 368. Relying on the two-factor test in T.L.O., the Court concluded that the strip search violated the Fourth Amendment because the scope of the intrusion was excessive in light of the student’s age and the nature of the alleged wrongdoing. Id. at 378. However, the Court also held that the qualified immunity doctrine protected the school officials from liability, illustrating how difficult it is for students to obtain monetary relief for illegal searches. Id. at 378–79; see also Feld, supra note 37, at 947–54; Nance, Students, Security, and Race, supra note 1, at 12 n.64.

76Nance, School Surveillance and the Fourth Amendment, supra note 1, at 95–96, 121; Nance, Students, Security, and Race, supra note 1, at 12–13. Nevertheless, as I have argued elsewhere, these decisions should not justify random, suspicionless searches that are highly intrusive, such as searches through students’ belongings, unless school officials have particularized evidence of a drug or weapons problem in their school. See Nance, Random, Suspicionless Searches, supra note 1, at 391–94.

77See, e.g., Hough v. Shakopee Pub. Sch., 608 F. Supp. 2d 1087, 1104 (D. Minn. 2009); In re Latasha W., 70 Cal. Rptr. 2d 886 (Cal. Ct. App. 1998); State v. J.A., 679 So. 2d 316, 319–20 (Fla. Dist. Ct. App. 1996); In re F.B., 726 A.2d 361, 366 (Pa. 1999).

78See, e.g., State v. Jones, 666 N.W.2d 142, 150 (Iowa 2003); In re Patrick Y., 746 A.2d 405, 414–15 (Md. 2000); In re Isiah B., 500 N.W.2d 637, 641 (Wis. 1993). Nevertheless, courts disagree regarding whether students retain an expectation of privacy in their lockers. See Kim, Losen & Hewitt, supra note 44, at 116; Feld, supra note 37, at 933–37; Nance, Random, Suspicionless Searches, supra note 1, at 411–12. Further, arguably, students should not lose their expectation of privacy in their personal belongings only because they place those belongings in a locker. See Nance, Random, Suspicionless Searches, supra note 1, at 411–12; Nance, Students, Security, and Race, supra note 1, at 13 n.66.

79Nance, School Surveillance and the Fourth Amendment, supra note 1, at 96; Nance, Students, Security, and Race, supra note 1, at 13.

80Aaron Kupchik, Homeroom Security: School Discipline in an Age of Fear 3 (2010) (observing that fears and insecurities motivate schools to adopt security measures in light of highly publicized acts of school violence); Kevin P. Brady, Sharon Balmer & Deinya Phenix, School-Police Partnership Effectiveness in Urban Schools, 39 Educ. & Urb. Soc’y 455, 456 (2007) (“An increasing fear of school violence coupled with the public’s misperceptions of the actual degree of violence in our nation’s schools has caused school officials, especially those located in urban areas, to implement more punitive-based school discipline policies and practices for responding to and preventing student crime and violence.”); Paul Hirschfield, School Surveillance in America: Disparate and Unequal, in Schools Under Surveillance: Cultures of Control in Public Education 38, 38 (Torin Monahan & Rodolfo D. Torres eds., 2010) (“The importation of surveillance tactics from criminal justice and the military into schools is most commonly attributed to elevated fears of school violence and a growing realization that ‘it can happen here.’”); Thomas Mowen, John Brent & Aaron Kupchik, School Crime and Safety, in The Handbook of Measurement Issues in Criminology and Criminal Justice 434, 440 (Beth M. Huebner & Timothy S. Bynum eds., 2016) (“[W]idespread insecurity coupled with anxieties about youth vulnerability have forced schools to take a proactive stance toward preventing the occurrence of violent acts on their premises.”); Nance, School Surveillance and the Fourth Amendment, supra note 1, at 92–94.

81See Kim Brooks, Vincent Schiraldi & Jason Ziedenberg, School House Hype: Two Years Later 5 (2000) (reporting that there “is a less than one in a million chance of a school aged youth dying or committing suicide on school grounds”); Barbara Fedders, Jason Langberg & Jennifer Story, School Safety in North Carolina: Realities, Recommendations & Resources 4 (2013) (“School violence that results in death is extremely rare. Young people are much more likely to be harmed in the home or on the street than they are in schools.” (footnote omitted)); Randall R. Beger, The “Worst of Both Worlds”: School Security and the Disappearing Fourth Amendment Rights of Students, 28 Crim. Just. Rev. 336, 338 (2003) (“Contrary to popular belief, schools remain among the safest places for children.”); Randy Borum et al., What Can Be Done About School Shootings? A Review of the Evidence, 39 Educ. Researcher 27, 27 (2010) (reporting that the number of homicides that occur on school grounds represents less than one percent of the annual homicides of children ages five to eighteen, and that “any given school can expect to experience a student homicide about once every 6,000 years”); Pedro A. Noguera, Schools, Prisons, and Social Implications of Punishment: Rethinking Disciplinary Practices, 42 Theory into Prac. 341, 343 (2003) (“Despite surveys that suggest a growing number of teachers and students fear violence in school, schools in the United States are generally safe places.” (citation omitted)); Arne Duncan, Resources for Schools to Prepare for and Recover from Crisis, Dep’t of Educ.: Homeroom (Dec. 17, 2012), http://www.ed.gov/blog/2012/12/resources-for-schools-to-prepare-for-and-recover-from-crisis/ (“Schools are among the safest places for children and adolescents in our country, and, in fact, crime in schools has been trending downward for more than a decade.”).

82Beger, supra note 81, at 338 (“Widely publicized incidents of juvenile violence in public schools have created the public misconception that such behavior is commonplace.”); Borum et al., supra note 81, at 27 (“[S]chool shootings receive such intense publicity, and are such inherently disturbing events, that they generate an inflated perception of danger.”); Matthew J. Mayer & Peter E. Leone, School Violence and Disruption Revisited: Equity and Safety in the School House, 40 Focus on Exceptional Child., Sept. 2007, at 1, 6 (“[M]edia coverage of school violence has shaped the public’s beliefs, and in many cases has led to a distorted perception of violence in schools, as well as adolescent violence more generally.”); Elizabeth S. Scott, Miller v. Alabama and the (Past and) Future of Juvenile Crime Regulation, 31 L. & Inequality 535, 541 (2013) (explaining that although serious acts of school violence are rare, after the shootings at Columbine High School, “legislatures across the country rushed to pass strict zero tolerance laws, making it a crime to threaten violence in school”); cf. Donna M. Bishop & Barry C. Feld, Juvenile Justice in the Get Tough Era, in Encyclopedia of Criminology and Criminal Justice 2766, 2768 (Gerben Bruinsma & Davis Weisburd eds., 2014) (explaining that violent incidents caused by juveniles attract a significant amount of media attention that can result in a “moral panic,” where “the media, politicians, and the public reinforce each other in an escalating alarmist response that exaggerates the magnitude of the threat and produces urgent calls to ‘do something’”).

83Nance, School Surveillance and the Fourth Amendment, supra note 1, at 92–93.

84See Jaana Juvonen, School Violence: Prevalence, Fears, and Prevention, RAND (2001), http://www.rand.org/pubs/issue_papers/IP219/index2.html; see also Rich Henson, Safety On ‘Everybody’s Mind’ For Schools’ Reopening Reacting to the Carnage at Columbine, Districts Across the Region Are Stepping Up Measures to Allow a Secure and Peaceful School Year, Philly.com (Aug. 29, 1999), http://articles.philly.com/1999-08-29/news/25483433_1_administrators-school-safety-summit-safety-measures; John D. Sutter, Columbine Massacre Changed School Security, CNN (Apr. 20, 2009, 9:30 AM), http://www.cnn.com/2009/LIVING/04/20/columbine.school.safety/index.html?iref=24hours.

85See Maggie Clark, Back to School: Security Heightened After Newtown, USA Today (Aug. 14, 2013, 4:00 PM), http://www.usatoday.com/story/news/nation/2013/08/12/stateline-school-security/2642155/; Mary Beth Marklein, Schools Tighten Security After Sandy Hook, USA Today (Sept. 24, 2013 12:12 PM), http://www.usatoday.com/story/news/nation/2013/09/23/schools-step-up-security-measures-in-wake-of-sandy-hook/2844423/.

86Torin Monahan & Rodolfo D. Torres, Introduction, in Schools Under Surveillance: Cultures of Control in Public Education 1, 2–3 (Torin Monahan & Rodolfo D. Torres eds., 2010).

87For a broader discussion on the criminalization of school discipline, see Henry A. Giroux, Racial Injustice and Disposable Youth in the Age of Zero Tolerance, 16 Int’l J. Qualitative Stud. Educ. 553 (2003); Paul J. Hirschfield, Preparing for Prison? The Criminalization of School Discipline in the USA, 12 Theoretical Criminology 79 (2008); Kathleen Nolan & Jean Anyon, Learning to Do Time: Willis’s Model of Cultural Reproduction in an Era of Postindustrialism, Globalization, and Mass Incarceration, in Learning to Labor in New Times 133 (Nadine Dolby, Greg Dimitriadis & Paul Willis eds., 2004); Matthew T. Theriot, School Resource Officers and the Criminalization of Student Behavior, 37 J. Crim. Just. 280 (2009); and Kerrin C. Wolf, Arrest Decision Making by School Resource Officers, 12 Youth Violence & Juv. Just. 137 (2014).

88See Kathleen Nolan, Police in the Hallways: Discipline in an Urban High School 22–24 (2011); Bishop & Feld, supra note 82, at 2770; Giroux, supra note 87, at 557–58; Mowen, Brent & Kupchik, supra note 80, at 435.

89Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 328; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 930–32. The widespread adoption of zero-tolerance policies is a good illustration of this movement. See Scott, supra note 82.

90Nance, School Surveillance and the Fourth Amendment, supra note 1, at 97–99; Nance, Students, Security, and Race, supra note 1, at 13–15; see also Juvonen, supra, note 84.

91See Office of Cmty. Oriented Policing Servs., DOJ, Fact Sheet: Secure Our Schools (2011), http://www.cops.usdoj.gov/pdf/2011AwardDocs/CSPP-SOS-CHP/2011-SOS-Post-FactSheet.pdf.

92See, e.g., Ind. Code § 10-21-1-2 (2016) (“The Indiana secured school fund is established to provide matching grants [with which a school may] (1) employ a school resource officer . . . (3) purchase equipment and technology to: (A) restrict access to school property; or (B) expedite notification of first responders.”); Miss. Code Ann. § 37-3-83 (2016) (“[T]he School Safety Grant Program shall offer . . . (a) [m]etal detectors; (b) [v]ideo surveillance cameras, communications equipment and monitoring equipment for classrooms, school buildings, school grounds and school buses . . . .”); N.Y. Educ. Law § 2814 (McKinney 2016) (“Programs eligible for funding pursuant to this section may include, but not limited to: . . . (iv) metal detectors, intercom and other intra-school communication devices and other devices . . . .”); 24 Pa. Stat. and Cons. Stat. Ann. § 13-1302-A (West 2016) (authorizing “targeted grants to school entities to fund programs which address school violence, including: . . . (9) Security planning, purchase of security-related technology which may include metal detectors, protective lighting, surveillance equipment . . . deadbolts and theft control devices and training”); Schools Against Violence in Education (SAVE) Act, Tenn. Code Ann. § 49-6-805, 811 (2016) (authorizing the Tennessee School Safety Center to provide grants to ensure that schools implement “[p]olicies and procedures relating to school building security, including . . . the use of school resource officers, security devices or security procedures”); Public School Security Equipment Grant Act of 2013, Va. Code Ann. § 22.1-280.2:2 (2016) (providing competitive grants to schools for security equipment).

93See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94–95; Nance, Students, Security, and Race, supra note 1, at 15–16.

94See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94–95; Nance, Students, Security, and Race, supra note 1, at 15–16. For example, the Every Student Succeeds Act, Pub. L. No. 114-95, 129 Stat. 1802 (2015), which is the latest reauthorization of the Elementary and Secondary Education Act and recently replaced the No Child Left Behind Act, requires states receiving federal education funds to formulate and conduct student academic assessments. See id. § 1005 (amending 20 U.S.C. § 6311(b)(2) (2012)). However, one of the hallmarks of the Every Student Succeeds Act is that it precludes the federal government from determining how to weigh those academic assessments for accountability purposes, leaving that decision to the states. See id. § 1005 (amending 20 U.S.C. § 6311(e)(1)(B)(iii)); see also Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 940 n.103.

95See Kupchik, supra note 80, at 28; The Advancement Project, Test, Punish, and Push Out: How “Zero Tolerance” and High-Stakes Testing Funnel Youth into the School-to-Prison Pipeline 28–33 (2010); Linda Darling-Hammond, Race, Inequality, and Educational Accountability: The Irony of ‘No Child Left Behind’, 10 Race Ethnicity & Educ. 245, 252–55 (2007); Deborah Gordon Klehr, Addressing the Unintended Consequences of No Child Left Behind and Zero Tolerance: Better Strategies for Safe Schools and Successful Students, 16 Geo. J. on Poverty L. & Pol’y 585, 602–03 (2009); Rachel F. Moran, Sorting and Reforming: High-Stakes Testing in the Public Schools, 34 Akron L. Rev. 107, 115 (2000); James E. Ryan, The Perverse Incentives of The No Child Left Behind Act, 79 N.Y.U. L. Rev. 932, 969–70 (2004).

96David N. Figlio, Testing, Crime and Punishment, 90 J. Pub. Econ. 837, 839 (2006).

97Id.

98Id.

99Id.

100Id. at 850.

101See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 94–95; Nance, Students, Security, and Race, supra note 1, at 15–16.

102See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 99–102.

103See Orfield & Lee, supra note 3, at 29–30; Diane Ravitch, Reign of Error: The Hoax of the Privatization Movement and the Danger to America’s Public Schools 290–91 (2013); Darling-Hammond, supra note 3, at 83; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 944; see also Noguera, supra note 81, at 342.

104See Bruce Baker et al., Is School Funding Fair? A National Report Card 5–6, 5 fig.2 (5th ed. 2016) (finding that in almost half of the states, school districts that serve more affluent students receive, on average, more money per student than school districts that serve primarily impoverished students); Orfield & Lee, supra note 3, at 29–31; Darling-Hammond, supra note 3, at 81–84; Nance, Persisting Inequalities, supra note 3; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 943–44; Kevin G. Welner & Prudence L. Carter, Achievement Gaps Arise from Opportunity Gaps, in Closing the Opportunity Gap: What America Must Do to Give Every Child an Even Chance 1, 6 (Prudence L. Carter & Kevin G. Welner eds., 2013) (“In a fundamentally unequal and unfair system characterized by widespread poverty and segregation, opportunity gaps are exacerbated when children are assigned to schools with substantially fewer resources than those in nearby middle-class communities.”).

105See Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 944–45; Nance, Students, Security, & Race, supra note 1, at 44–46; Noguera, supra note 81, at 342 (observing that it is the severe needs of students and the inability of schools to meet those needs that causes students to be disruptive and sometimes dangerous at school); see also Matthew P. Steinberg, Elaine Allensworth & David W. Johnson, Student and Teacher Safety in Chicago Public Schools: The Roles of Community Context and School Social Organization 46 (2011) (maintaining that low-performing students are less likely to be engaged in school and more likely to be frustrated and misbehave); Matthew P. Steinberg, Elaine Allensworth & David W. Johnson, What Conditions Support Safety in Urban Schools?: The Influence of School Organizational Practices on Student and Teacher Reports of Safety in Chicago, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 118, 125 (Daniel J. Losen ed., 2015) (explaining that low-achieving students are less likely to be engaged and more likely to act out).

106See infra notes 414–15 and accompanying text; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 336–62 (discussing initiatives to promote safe learning climates that do not involve harsh disciplinary measures); Nance, Students, Security, and Race, supra note 1, at 48–55 (discussing alternative measures to reduce school violence).

107See Ravitch, supra note 103, at 295 (observing that schools serving primarily impoverished students are more likely to rely on harsh discipline policies that include strict surveillance methods and exclusionary discipline practices); NAACP Legal Def. & Educ. Fund, Dismantling the School-to-Prison Pipeline 5 (2005); Hirschfield, supra note 87, at 92 (observing that some educators rely on extreme methods of punishment and control because they believe that they “lack the resources to reverse the downward trajectories of the most troublesome students without compromising the quality of teaching and services aimed at more deserving or promising students”); Noguera, supra note 81, at 345 (observing that schools teaching large numbers of struggling students “operate more like prisons than schools,” “are more likely to rely on guards, metal detectors, and surveillance cameras to monitor and control students,” and have a “fixation on control [that] tends to override all other educational objectives and concerns”).

108Monahan & Torres, supra note 86, at 2–3; Nance, Students, Security, and Race, supra note 1, at 16.

109See Jason P. Nance, School Security Considerations After Newtown, 65 Stan. L. Rev. Online 103, 106 (2013) [hereinafter Nance, School Security Considerations]; Nance, Students, Security, and Race, supra note 1, at 16–17.

110See Duncan, supra note 81 (explaining that not all tragedies that happen at school can be prevented); see also Nance, Students, Security, and Race, supra note 1, at 16–17. For example, the Columbine shootings occurred in a school with armed guards. Amanda Terkel, Columbine High School Had Armed Guard During Massacre in 1999, Huffington Post (Dec. 23, 2012), http://www.huffingtonpost.com/2012/12/21/columbine-armed-guard_n_2347096.html.

111See supra Part I.B.

112For instance, during the 2012–2013 school year, only thirty-one out of the 1186 homicides of five to eighteen-year-old youth occurred at school. Anlan Zhang, Lauren Musu-Gillette & Barbara A. Oudekerk, Indicators of School Crime and Safety: 2015, at iv (2016). Notably, the School-Associated Violent Deaths Study (SAVD), which gathered this information, defines “at school” broadly. Id. at iv n.8. SAVD defined “at school” for purposes of its survey as “on school property, on the way to or from regular sessions at school, and while attending or traveling to or from a school-sponsored event.” Id. Similarly, in 2014, four in one thousand youth between the ages of twelve and eighteen were victims of serious violent crimes at school, but six in one thousand students from the same age group were victims of those crimes outside of school. Id. at 24; see also Nance, Students, Security, and Race, supra note 1, at 17; supra note 81 and accompanying text.

113See infra Part III.

114See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 336–62 for a complete discussion of these alternative measures.

115See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 104–05.

116See Roger D. Goddard, Megan Tschannen-Moran & Wayne K. Hoy, A Multilevel Examination of the Distribution and Effects of Teacher Trust in Students and Parents in Urban Elementary Schools, 102 Elementary Sch. J. 3, 3–4 (2001) (observing that trust is an important element of the teaching and learning process); Anne Gregory & Rhona S. Weinstein, The Discipline Gap and African Americans: Defiance or Cooperation in the High School Classroom, 46 J. Sch. Psychol. 455, 458–59 (2008); Megan Tschannen-Moran & Wayne K. Hoy, A Multidisciplinary Analysis of the Nature, Meaning, and Measurement of Trust, 70 Rev. Educ. Res. 547, 547 (2000); see also Nance, Random, Suspicionless Searches, supra note 1, at 395; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 104; Nance, Students, Security, and Race, supra note 1, at 19.

117See David Domenici & James Forman Jr., What It Takes to Transform a School Inside a Juvenile Justice Facility: The Story of the Maya Angelou Academy, in Justice for Kids: Keeping Kids Out of the Juvenile Justice System 283, 289 (Nancy E. Dowd ed., 2011) (“High achieving schools are places where a culture of trust dominates.”); Over-Policing in Schools on Students’ Education and Privacy Rights, N.Y. C.L. Union [hereinafter Over-Policing in Schools], http://www.nyclu.org/content/over-policing-schools-students-education-and-privacy-rights (last visited Aug. 1, 2016) (featuring the June 14, 2006, testimony of Donna Lieberman on behalf of the New York Civil Liberties Union before the New York City Council Committee on Education and Public Safety regarding the impact of over-policing in schools on students’ education and privacy rights); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 104.

118Darling-Hammond, supra note 6, at 65.

119See Gregory & Weinstein, supra note 116, at 458, 469–70. These findings are by no means limited to children in schools. Tom Tyler has demonstrated that citizens’ evaluations of the courts and police are not primarily related to the outcomes or judgments; rather, they are linked more closely to how fairly they perceive they are being treated. See Tom R. Tyler, Psychological Perspectives on Legitimacy and Legitimation, 57 Ann. Rev. Psychol. 375, 379–80 (2006); Tom R. Tyler, Public Trust and Confidence in Legal Authorities: What Do Majority and Minority Group Members Want from the Law and Legal Institutions?, 19 Behav. Sci. & L. 215, 233 (2001).

120See Beger, supra note 81, at 340; Hirschfield, supra note 80, at 46 (maintaining that strict security measures “sour students’ attitudes toward school and school authorities, and undermine a positive, respectful academic environment”); Pedro A. Noguera, Preventing and Producing Violence: A Critical Analysis of Responses to School Violence, 65 Harv. Educ. Rev. 189, 190–91 (1995); Carol Ascher, Gaining Control of Violence in the Schools: A View from the Field, ERIC Digest, Sept. 1994, at 1, 4, http://www.eric.ed.gov/PDFS/ED377256.pdf (observing that strict security measures signal to students that teachers are afraid of their students); see also Nance, Random, Suspicionless Searches, supra note 1, at 395; Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 104; Nance, Students, Security, and Race, supra note 1, at 19–20.

121Hirschfield, supra note 80, at 46.

122Martin R. Gardner, Student Privacy in the Wake of T.L.O.: An Appeal for an Individualized Suspicion Requirement for Valid Searches and Seizures in the Schools, 22 Ga. L. Rev. 897, 943 (1988).

123Id. (footnotes omitted). Further, it is important to emphasize that while airline passengers decide voluntarily to board a plane or drive through a checkpoint, students are required to submit to these generalized searches and intense surveillance conditions against their will because of mandatory school attendance laws. See Feld, supra note 37, at 903–04.

124Jen Weiss, Scan This: Examining Student Resistance to School Surveillance, in Schools Under Surveillance: Cultures of Control in Public Education 213, 227 (Torin Monahan & Rodolfo D. Torres eds., 2010).

125Id. at 213–14; see also Brooks, Schiraldi & Ziedenberg, supra note 81, at 3 (“Parents and school boards continue to call for more metal detectors, locker searches and student identification badges, even as students say they feel less safe and report more crime in schools that use these ‘secure’ school procedures.”).

126See Over-Policing in Schools, supra note 117.

127Id.; see also Brooks, Schiraldi & Ziedenberg, supra note 81, at 3 (quoting a student saying, “[w]hen I get up to go to school in the morning, I don’t want to feel like I’m going to a correctional facility”); Finn & Servoss, supra note 37, at 44 (maintaining that intense surveillance methods create prison-like conditions that make “students feel defensive and contribute to their emotional and physical disengagement from school”).

128See Timothy J. Servoss, School Security and Student Misbehavior: A Multi-Level Examination, 46 Youth & Soc.: OnlineFirst 1, 3 (2014).

129Id.

130See Tom R. Tyler & Lindsay Rankin, Legal Socialization and Delinquency, in The Oxford Handbook of Juvenile Crime and Juvenile Justice 353, 361 (Barry C. Feld & Donna M. Bishop eds., 2012).

131Id.; see also David Kipnis, Trust & Technology, in Trust in Organizations: Frontiers of Theory & Research 39, 46–47 (Roderick M. Kramer & Tom R. Tyler eds., 1996).

132See Tyler & Rankin, supra note 130, at 361; Jason Sunshine & Tom R. Tyler, The Role of Procedural Justice and Legitimacy in Shaping Public Support for Policing, 37 Law & Soc’y Rev. 513, 514 (2003).

133See Tyler & Rankin, supra note 130, at 361–62.

134See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 102–03. For a definition of the term “school-to-prison pipeline,” see supra text accompanying notes 33–35.

135Feld, supra note 37, at 952–53; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83. See supra note 36 for a definition of the term “zero tolerance.”

136See Am. Psychol. Ass’n Zero Tolerance Task Force, supra note 36, at 852. This is only one aspect of policies and practices that have combined to put many students, especially students of color, disabled students, and other marginalized groups, on a pathway from school to prison. For an extended discussion of the school-to-prison pipeline and its disproportionate effect on marginalized students, see generally Sarah E. Redfield & Jason P. Nance, American Bar Association Joint Task Force on Reversing the School-to-Prison Pipeline: Preliminary Report (2016); Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 315; and Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1.

137In re Expulsion of A.D., 883 N.W.2d 251, 253 (Minn. 2016).

138Id. at 254.

139Id.

140Id. The Minnesota Supreme Court later overturned A.D.’s expulsion because the school district did not comply with Minnesota Statute § 121A.45, subdiv. 2(a), which provides school districts with authority to suspend or expel students only for a “willful violation of any reasonable school board regulation,” and the Court did not consider A.D.’s act to be a willful violation. Id. at 256, 263–64.

141See Timothy J. Servoss & Jeremy D. Finn, School Security: For Whom and With What Results?, 13 Leadership & Pol’y Schs. 61, 82–83 (2014).

142See Finn & Servoss, supra note 37, at 53. These suspensions included in-school suspensions and out-of-school suspensions. Id. at 47–48, 53.

143Id. at 53.

144See Feld, supra note 37, at 884–95 (explaining how the combination of school police officers, students’ diminished constitutional rights, school accountability laws, zero-tolerance policies, and strict security measures have put more students on a pathway to prison); Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 321–24; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 956.

145See Noguera, supra note 81, at 345–46; Dear Colleague Letter, supra note 4, at 4.

146See Steinberg, Allensworth & Johnson, supra note 105, at 46 (explaining that less engaged students are more likely to be frustrated and misbehave); Noguera, supra note 81, at 342, 345–46.

147See, e.g., Justice Policy Inst., Education Under Arrest: The Case Against Police in Schools 17–18 (2011), http://www.justicepolicy.org/uploads/justicepolicy/documents/educationunderarrest_fullreport.pdf; Robert Balfanz, Vaughn Byrnes & Joanna Hornig Fox, Sent Home and Put Off Track, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 17, 22–29 (Daniel J. Losen ed., 2015) (finding empirically in a longitudinal study of 181,897 Florida students that, after controlling for student demographics and other indicators that a student is not on track to graduate, each suspension decreases the odds that a student will graduate by 20%); Miner P. Marchbanks III et al., The Economic Effects of Exclusionary Discipline on Grade Retention and High School Dropout, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 59, 64 (Daniel J. Losen ed., 2015) (demonstrating empirically that students subject to exclusionary discipline were 23.5% more likely to drop out of school); Tracy L. Shollenberger, Racial Disparities in School Suspension and Subsequent Outcomes: Evidence from the National Longitudinal Survey of Youth, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 31, 36 (Daniel J. Losen ed., 2015) (showing empirically that exclusionary discipline negatively affected graduation rates, but has a magnified impact on African-American males and Latino males); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 321–22.

148See Clive R. Belfield, Henry M. Levin & Rachel Rosen, The Economic Value of Opportunity Youth 20 (2012), http://www.serve.gov/new-images/council/pdf/econ_value_opportunity_youth.pdf; John M. Bridgeland, John J. Dilulio, Jr. & Karen Burke Morison, The Silent Epidemic: Perspectives of High School Dropouts, at i (2006), https://docs.gatesfoundation.org/documents/thesilentepidemic3-06final.pdf; Nat’l Ctr. Juvenile Justice, Juvenile Offenders and Victims: 2014 National Report 14–15 (Melissa Sickmund & Charles Puzzanchera eds., 2014), http://www.ojjdp.gov/ojstatbb/nr2014/downloads/NR2014.pdf; Andrew Sum et al., The Consequences of Dropping Out of High School: Joblessness and Jailing for High School Dropouts and the High Cost for Taxpayers 7–11 (2009); Bruce Western, Punishment and Inequality in America 17–18 (2006); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 322.

149See Belfield, Levin & Rosen, supra note 148, at 20; Bridgeland, Dilulio & Morison, supra note 148, at i; Sum et al., supra note 148, at 7–11; Western, supra note 148, at 17–18; Thomas Mowen & John Brent, School Discipline as a Turning Point: The Cumulative Effect of Suspension on Arrest, 53 J. Res. Crime & Delinq. 628 (2016) (demonstrating empirically that students who are suspended are more likely to be arrested in later years); Shollenberger, supra note 147, at 36–37 (finding empirically that suspended students are more likely to be involved in the justice system as adults); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 323.

150Am. Acad. of Pediatrics, Out-of-School Suspension and Expulsion, 112 Pediatrics 1206, 1207 (2003) (observing that when students are not monitored by trained professionals and are at home without parent supervision, they are far more likely to commit crimes); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 323–24.

151See Tony Fabelo et al., Breaking Schools’ Rules: A Statewide Study of How School Discipline Relates to Students’ Success and Juvenile Justice Involvement 70 (2011).

152Id.; see also Alison Evans Cuellar & Sara Markowitz, School Suspension and the School-to-Prison Pipeline, 43 Int’l Rev. L. & Econ. 98, 99 (2015) (finding empirically that students who received out-of-school suspensions were more likely to commit criminal offenses on suspension days than on non-suspension days).

153The U.S. Department of Education recently cautioned school districts to “avoid overuse of exclusionary discipline . . . reserv[ing] the use of out-of-school suspensions, expulsions, and alternative placements for the most egregious disciplinary infractions that threaten school safety and when mandated by federal or state law.” U.S. Dep’t of Educ., Guiding Principles: A Resource Guide for Improving School Climate and Discipline 15 (2014) [hereinafter Guiding Principles].

154Steinberg et al., supra note 105, at 128–29.

155Id.

156Brea L. Perry & Edward W. Morris, Suspending Progress: Collateral Consequences of Exclusionary Punishment in Public Schools, 79 Am. Soc. Rev. 1067, 1072 (2014).

157Id. at 1076–84.

158Id. at 1083.

159Advancement Project, Education on Lockdown: The Schoolhouse to Jailhouse Track 8 (2005), http://www.advancementproject.org/sites/default/files/publications/FINALEOLrep.pdf (arguing that strict security measures may “produce a perception of safety, [but] there is little or no evidence that they create safer learning environments or change disruptive behaviors”); Ascher, supra note 120, at 4 (“Rather than offering reassurance, metal detectors and other mechanical devices, as well as security forces, are seen as providing a false sense of safety, if not a harsh symbol of the failure to create safe schools.”); Mowen, Brent & Kupchik, supra note 80, at 443 (“[T]here is no clear evidence that the criminalization of school discipline is effective at preventing school violence.”); Richard E. Redding & Sarah M. Shalf, The Legal Context of School Violence: The Effectiveness of Federal, State, and Local Law Enforcement Efforts to Reduce Gun Violence in Schools, 23 Law & Pol’y 297, 319 (2001) (explaining that “[i]t is hard to find anything better than anecdotal evidence” showing that strict security measures such as metal detectors and guards reduce violence in schools); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 106–07. Notably, research on the effectiveness of school security measures is extremely limited, especially causal research. See Mayer & Leone, supra note 82, at 12.

160Abigail Hankin, Marci Hertz & Thomas Simon, Impacts of Metal Detector Use in Schools: Insights from 15 Years of Research, 81 J. Sch. Health 100, 105 (2011).

161Id.

162Id.; see also John Blosnich & Robert Bossarte, Low-Level Violence in Schools: Is There an Association Between School Safety Measures and Peer Victimization?, 81 J. Sch. Health 107 (2011) (finding that school security measures did not reduce violent behaviors related to bullying); Matthew J. Mayer & Peter E. Leone, A Structural Analysis of School Violence and Disruption: Implications for Creating Safer Schools, 22 Educ. & Treatment Child. 333, 350, 352 (1999) (finding that student victimization and school disorder were higher in schools using strict security measures). But see Ctrs. for Disease Control & Prevention, Violence-Related Attitudes and Behaviors of High School Students—New York City, 1992, 42 Morbidity & Mortality Wkly. Rep. 773, 774, 776 (1993) (reporting that students who attended schools using metal detectors were less likely to carry a weapon inside a school—7.8% versus 13.6%—but the use of metal detectors did not reduce school violence); Renee Wilson-Brewer & Howard Spivak, Violence Prevention in Schools and Other Community Settings: The Pediatrician as Initiator, Educator, Collaborator, and Advocate, 94 Pediatrics 623, 626–27 (1994) (stating that one school system in New York City reported that after the school security staff began using hand-held metal detectors to conduct unannounced lobby searches of students at the beginning of the school day, weapon-related incidents decreased in thirteen of fifteen schools).

163Crystal A. Garcia, School Safety Technology in American: Current Use and Perceived Effectiveness, 14 Crim. Just. Pol’y Rev. 30, 40 (2003).

164Nance, School Surveillance and the Fourth Amendment, supra note 1, at 107–08; Nance, Students, Security, and Race, supra note 1, at 23–24.

165See Marcus Wright, Experts Say Intrusive Security at Public Schools Reproduces Social Inequality, Truthout (Nov. 21, 2012, 11:22 AM), http://www.truth-out.org/news/item/12886-experts-say-intrusive-security-at-public-schools-reproduces-social-inequality.

166Sara Neufeld & Sumathi Reddy, Violent Week Renews Metal Detector Debate, Balt. Sun (Oct. 14, 2006), http://articles.baltimoresun.com/2006-10-14/news/0610140131_1_metal-detectors-school-students-park-elementary-school.

167See Ascher, supra note 120, at 5 (“[T]hose few students intent on bringing in weapons are inevitably a step ahead of the security devices, which means that enforcement activities alone cannot create a safe school.”); Neufeld & Reddy, supra note 166 (reporting that students interviewed claimed that it was “easy to get around” metal detectors); Noguera, supra note 120, at 193 (reporting that the students he spoke with understood how to bring a weapon into a school using strict security measures without detection); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 107–08; Nance, Students, Security, and Race, supra note 1, at 23–24.

168Neufeld & Reddy, supra note 166.

169See Kupchik, supra note 80, at 7–8 (maintaining that punitive measures may increase rather than decrease student misbehavior); Ascher, supra note 120, at 5 (observing that strict security measures “increase, rather than alleviate, tension in schools”); Beger, supra note 81, at 340 (explaining that “aggressive security measures produce alienation and mistrust among students”); Michael Easterbrook, Taking Aim at Violence, 32 Psychol. Today, July/Aug. 1999, at 52, 56 (arguing that strict security measures alienate students); Clifford H. Edwards, Student Violence and the Moral Dimensions of Education, 38 Psychol. Schs. 249, 250 (2001) (observing that “intrusive strategies are likely to undermine the trust needed to build cooperative school communities capable of really preventing violence”); Mayer & Leone, supra note 162, at 349–50, 352 (finding that student disorder and victimization were higher in schools using strict security measures and arguing that “an unwelcoming, almost jail-like, heavily scrutinized environment[] may foster the violence and disorder school administrators hope to avoid”); Amanda B. Nickerson & Matthew P. Martens, School Violence: Associations with Control, Security/Enforcement, Educational/Therapeutic Approaches, and Demographic Factors, 37 Sch. Psychol. Rev. 228, 238–39 (2008) (finding that strict security measures and punitive measures were positively associated with more school disorder and school crime); Noguera, supra note 120, at 190–91 (observing that a “get-tough” approach can hamper school officials’ efforts to create safe environments because coercive measures creates mistrust and resistance among the student body); Noguera, supra note 81, at 345 (“When children are presumed to be wild, uncontrollable, and potentially dangerous, it is not surprising that antagonistic relations with the adults who are assigned to control them develop.”); Christopher J. Schreck, J. Mitchell Miller & Chris L. Gibson, Trouble in the School Yard: A Study of the Risk Factors of Victimization at School, 49 Crime & Delinq. 460, 471 (2003) (empirically finding that students attending schools that undertake locker searches report more student victimization); see also Nance, Random, Suspicionless Searches, supra note 1, at 399; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 106–07; Nance, Students, Security, and Race, supra note 1, at 21–22.

170See Servoss, supra note 128, at 5; see also Albert Bandura, Social Foundations of Thought and Action: A Social Cognitive Theory 27, 30 (1986); Albert Bandura, The Self System in Reciprocal Determinism, 33 Am. Psychol. 344, 356–57 (1978).

171See Bandura, supra note 170; Servoss, supra note 128, at 5.

172See Servoss, supra note 128, at 5.

173See Kupchik, supra note 80, at 6 (observing that schools do not often address the underlying issues associated with student misbehavior); see also Nance, School Surveillance and the Fourth Amendment, supra note 1, at 108; Nance, Students, Security, and Race, supra note 1, at 24.

174See Robert A. Fein et al., U.S. Secret Serv. & U.S. Dep’t of Educ., Threat Assessment in Schools: A Guide to Managing Threatening Situations and to Creating Safe School Climates, at iii (2004).

175Id. at 5–6.

176Id. at 6, 11; see also Fedders, Langberg & Story, supra note 81, at 6 (“Positive relationships among students, families, teachers, administrators, and staff are the most effective tools in creating a safe school environment.”).

177Steinberg, Allensworth & Johnson, supra note 105, at 1; see also Mark T. Greenberg et al., Enhancing School-Based Prevention and Youth Development Through Coordinated Social, Emotional, and Academic Learning, 58 Am. Psychol. 466, 468, 470 (2003) (finding that schools can successfully improve student behavior by creating caring communities, enhancing the school climate, and building trust among school staff, families, and students); Steinberg, Allensworth & Johnson, supra note 105, at 126 (reporting that school leadership, teacher collaboration, school-family interactions, and student-teacher relationships explained 80% of the variance associated with school safety as reported by students and teachers).

178See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109; supra notes 120–33 and accompanying text.

179See Hankin, Hertz & Simon, supra note 160, at 105 (“Metal detector programs are expensive, and funds spent on metal detectors would not be available for other programs and strategies that have been shown to be effective at reducing youth risk for violence and promoting pro-social behaviors.”); Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109.

180See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 345–60.

181See Thalia González, Socializing Schools: Addressing Racial Disparities in Discipline Through Restorative Justice, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 151, 151–53 (Daniel J. Losen ed., 2015); Restorative Discipline in Schools, Inst. for Restorative Just. & Restorative Dialogue, https://irjrd.org/restorative-discipline-in-schools/ (last visited July 26, 2016).

182See Catherine P. Bradshaw, Mary M. Mitchell & Philip J. Leaf, Examining the Effects of Schoolwide Positive Behavioral Interventions and Supports on Student Outcomes, 12 J. Positive Behav. Interventions 133 (2010); What Is School-Wide PBIS, Positive Behav. Interventions & Supports, http://www.pbis.org/school/default.aspx (last visited Feb. 17, 2016); see also Mayer & Leone, supra note 82, at 13 (maintaining that Schoolwide Positive Behavioral Support can “transform[] the school environment to support overall student success, behaviorally, socially, and academically”).

183Joseph A. Durlak et al., The Impact of Enhancing Students’ Social and Emotional Learning: A Meta-Analysis of School-Based Universal Interventions, 82 Child Dev. 405, 406 (2011); David M. Osher et al., Avoid Quick Fixes: Lessons Learned from a Comprehensive Districtwide Approach to Improve Conditions for Learning, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 192, 192 (Daniel J. Losen ed., 2015).

184Michael Eskenazi, Gillian Eddins & John M. Beam, Equity or Exclusion: The Dynamics of Resources, Demographics, and Behavior in the New York City Public Schools 2 (2003) (maintaining that teacher qualifications have a strong positive effect on student behavior); Fedders, Langberg & Story, supra note 81, at 8; Daniel J. Losen, The Civil Rights Project, Discipline Policies, Successful Schools, and Racial Justice 1 (2011); Daniel J. Losen & Jonathan Gillespie, The Civil Rights Project, Opportunities Suspended: The Disparate Impact of Disciplinary Exclusion from School 35–37 (2012); Steinberg, Allensworth & Johnson, supra note 105, at 47–48; Anne Gregory et al., The Promise of a Teacher Professional Development Program in Reducing Racial Disparity in Classroom Exclusionary Discipline, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 166, 166–67 (Daniel J. Losen ed., 2015) (explaining that good teachers “are able to diffuse disruptive and disobedient behavior quickly, without relying on an office discipline referral that excludes a student from the classroom,” and “[r]esearch has shown that engaging and motivating teachers can prevent students from disrupting class in the first place”); David Osher et al., How Can We Improve School Discipline?, 39 Educ. Researcher 48, 49 (2010).

185See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 338–52; Nance, Students, Security, and Race, supra note 1, at 105–10.

186Nance, Random, Suspicionless Searches, supra note 1, at 402–04; Nance, School Security Considerations, supra note 1, at 106–07; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109–12.

187See Aaron Kupchik & Geoff Ward, Race, Poverty, and Exclusionary School Security: An Empirical Analysis of U.S. Elementary, Middle, and High Schools, 12 Youth Violence & Juv. Just. 332, 337–38 (2014); Loïc Wacquant, Deadly Symbiosis: When Ghetto and Prison Meet and Mesh, 3 Punishment & Soc’y 95, 108 (2011) (arguing that inner-city, majority-minority schools operate not as institutions of education, but as institutions of confinement and control); see also Nance, Random, Suspicionless Searches, supra note 1, at 403–04; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 109–10; Nance, Students, Security, and Race, supra note 1, at 25.

188See Hirschfield, supra note 80, at 40 (arguing that the disproportionate use of intense surveillance methods prepares urban minority students to become prisoners, soldiers, or service sector workers); Kupchik & Ward, supra note 187, at 338 (“[M]arginalized youth are presumed to be young criminals and treated as such through exposure to the hard edge of exclusive practices (e.g., police surveillance and metal detectors), while youth with social, political, and cultural capital are presumed to be near normal and habituated for social absorption in their selective exposure to inclusive security regimes.”); see also Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110; Nance, Students, Security, and Race, supra note 1, at 25.

189See supra Part II.A; see also Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110.

190See Finn & Servoss, supra note 37, at 53 (finding that security levels were positively associated with greater racial disparities in suspensions); Edward W. Morris & Brea L. Perry, The Punishment Gap: School Suspension and Racial Disparities in Achievement, 63 Soc. Probs. 68, 81–84 (2016) (providing empirical evidence that school suspensions contribute to racial inequalities in academic achievement); Servoss & Finn, supra note 141, at 82–83 (finding that higher levels of school surveillance were positively associated with higher student suspension rates).

191See Noguera, supra note 81, at 343–44; see also Victor M. Rios, Punished: Policing the Lives of Black and Latino Boys, at xiv, 75 (2011) (observing that students of color experience disproportionate surveillance because “schools, police, probation officers, families, community centers, the media, businesses, and other institutions systematically treat young people’s everyday behaviors as criminal activity,” making them feel “criminalized from a young age”); Khan, supra note 7 (reporting that students of color often perceive their school simply as an extension of a “police state” because they are subject to intense surveillance environments both in their neighborhoods and in their schools); Nance, Random, Suspicionless Searches, supra note 1, at 403–04; Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110; Nance, Students, Security, and Race, supra note 1, at 25–26.

192Darling-Hammond, supra note 6, at 65.

193See Khan, supra note 7.

194Id.; cf. Nance, Persisting Inequalities, supra note 3 (reporting that when low-income minority students who attended low-resourced schools were shown photos of high-resourced schools, they responded with comments such as, “[t]hose must be schools for white kids,” and “[t]hey wouldn’t give those materials to us”).

195See Constance Flanagan et al., School and Community Climates and Civic Commitments: Patterns for Ethnic Minority and Majority Students, 99 J. Educ. Psychol. 421, 423 (2007) (observing that studies show that minority groups report “a lower sense of school belonging than . . . their European American peers”); Susan Rakosi Rosenbloom & Niobe Way, Experiences of Discrimination Among African American, Asian American, and Latino Adolescents in an Urban High School, 35 Youth & Soc. 420, 434 (2004) (stating that “[w]hen African American and Latino students were asked about their experiences with discrimination, they described hostile relationships with adults in positions of authority such as . . . teachers in school”); Rosa Hernández Sheets, Urban Classroom Conflict: Student-Teacher Perception: Ethnic Integrity, Solidarity, and Resistance, 28 Urb. Rev. 165, 175 (1996) (reporting that minority students in a study on classroom conflict believed that their teachers did not care about them or respect them and that they abused their authority); see also Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 111; Nance, Students, Security, and Race, supra note 1, at 26–27; cf. Noguera, supra note 120, at 201 (observing that many black communities believe that black children are not treated fairly in schools).

196See supra notes 120–33 and accompanying text; see also Kupchik & Ward, supra note 187, at 333; Nance, Random, Suspicionless Searches, supra note 1, at 403; Nance, School Surveillance and the Fourth Amendment, supra note 1, at 111–12; Nance, Students, Security, and Race, supra note 1, at 26–27.

197See Nance, School Surveillance and Students’ Fourth Amendment Rights, supra note 1, at 110–11; Nance, Students, Security, and Race, supra note 1, at 25–26.

198See, e.g., Ronnie Casella, Being Down: Challenging Violence in Urban Schools 71–72 (2001) (arguing that as students “are deemed deviant, they are treated as deviants, and therefore deviate as is expected of them”); J. Alleyne Johnson, Life After Death: Critical Pedagogy in an Urban Classroom, 65 Harv. Educ. Rev. 213, 220–21 (1995) (observing how students’ self-perceptions changed for the worse upon being assigned to a special class once the students learned that the general perception of the class was that it was for students who were “at risk,” “learning disabled,” and “disruptive”).

199Noguera, supra note 81, at 343 (citations omitted).

200Khan, supra note 7.

201See Nance, Random, Suspicionless Searches, supra note 1, at 425–26.

202Cf. Nance, Persisting Inequalities, supra note 3 (arguing that tolerating inequalities with respect to school resources along racial lines sends minority students a message inconsistent with the values that public education should uphold).

203See Lani Guinier, From Racial Liberalism to Racial Literacy: Brown v. Board of Education and the Interest-Divergence Dilemma, 91 J. Am. Hist. 92, 113–16 (2004).

204Sharon E. Rush, Huck Finn’s “Hidden Lessons”: Teaching and Learning Across the Color Line 123–24 (2006) (emphasis omitted).

205See id. at 124; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 351–52.

206Sharon Elizabeth Rush, The Heart of Equal Protection: Education and Race, 23 N.Y.U. Rev. L. & Soc. Change 1, 33, 42 (1997).

207See, e.g., supra notes 3–6 and accompanying text.

208See Nance, Students, Security, and Race, supra note 1, at 27–43. Students, Security, and Race contains a detailed description of the study’s dataset, variables, and results, as well as its limitations. Here, I provide only a brief description of this study.

209The NCES “is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S. Department of Education and the Institute of Education Sciences.” About Us, Nat’l Ctr. Educ. Stat., https://nces.ed.gov/about/ (last visited Jan. 5, 2016).

210See U.S. Dep’t of Educ. Nat’l Ctr. for Educ. Statistics, School Survey on Crime and Safety Principal Questionnaire: 2009–10 School Year [hereinafter 2009–2010 SSOCS Questionnaire], http://nces.ed.gov/surveys/ssocs/pdf/SSOCS_2010_Questionnaire.pdf (last visited Feb. 22, 2016); see also Nance, Students, Security, and Race, supra note 1, at 28. The dataset I analyzed was the restricted-access version. The restricted-access version contains sensitive information submitted by school officials about crimes that occurred in their schools or on their school grounds. See Statistical Standards Program: Getting Started, Nat’l Ctr. Educ. Statistics, http://nces.ed.gov/statprog/instruct_gettingstarted.asp (last visited Feb. 22, 2016). Although restricted datasets are available only to researchers who meet certain conditions, see Statistical Standards Program: Getting Started, supra, datasets containing less sensitive data are available to the public. See School Survey on Crime and Safety (SSOCS), Nat’l Ctr. Educ. Stat., http://nces.ed.gov/surveys/ssocs/data_products.asp (last visited Feb. 22, 2016); see also Nance, Students, Security, and Race, supra note 1, at 29.

211Nat’l Ctr. for Educ. Statistics, 2009–2010 School Survey on Crime and Safety (SSOCS): Restricted-Use Data File User Manual 8 (2011) [hereinafter 2009–2010 Restricted-Use Manual] (on file with author). One of the conditions imposed by NCES is that all raw numbers be rounded to the nearest ten. U.S. Dep’t of Educ., Restricted-Use Data Procedures Manual 20 (2011), http://nces.ed.gov/pubs96/96860rev.pdf.

2122009–2010 Restricted-Use Manual, supra note 211, at 1, 9–13.

2132009–2010 SSOCS Questionnaire, supra note 210, at 5, 8.

214I examined four different combinations of security practices: (1) metal detectors and guards/school police; (2) metal detectors, guards/school police, and random sweeps for contraband; (3) metal detectors, guards/school police, random sweeps for contraband, and security cameras; and (4) metal detectors, guards/school police, random sweeps for contraband, security cameras, and locked gates. Nance, Students, Security, and Race, supra note 1, at 31. I note here one significant limitation of using the SSOCS data to test my hypothesis. In two of the survey questions, principals indicated whether it was a practice in their schools to perform “one or more random metal detector checks on students” or “random sweeps for contraband” during the 2009–2010 school year. 2009–2010 SSOCS Questionnaire, supra note 210, at 5. Thus, it is possible that by responding affirmatively to either (or both) of these survey items, the school personnel in fact performed only one random metal detector check or one random sweep for contraband during the school year. And if the school performed only one metal detector check or one random sweep during the school year, it is more difficult to make the argument that such actions create an intense surveillance environment. Nevertheless, if school officials indicate that they relied on several of these surveillance methods during the school year it seems more likely that they relied on these measures more than one time. Further, if school officials have access to equipment to perform random metal detector checks and use it at least once during the school year, it seems quite plausible that school officials used this equipment more than once (and used it regularly). Nance, Students, Security, and Race, supra note 1, at 32. The NCES should craft more careful questions to resolve this ambiguity in future questionnaires.

215Nance, Students, Security, and Race, supra note 1, at 32. A school’s student minority population included students who were African-American, Hispanic, Asian/Pacific Islanders, and American-Indian/Alaska native students. See Simone Robers et al., Indicators of School Crime and Safety: 2011, at 112 tbl.6 (2012), http://www.bjs.gov/content/pub/pdf/iscs11.pdf.

216Nance, Students, Security, and Race, supra note 1, at 32. Free or reduced price lunch is a common proxy for measuring student poverty. See, e.g., Michael Heise, Litigated Learning, Law’s Limits, and Urban School Reform Challenges, 85 N.C. L. Rev. 1419, 1441 (2007); see also Nutrition and Schools, New Am., http://atlas.newamerica.org/federal-school-nutrition-programs (last visited July 14, 2016) (“Researchers often use free or reduced-price lunch (FRPL) enrollment figures as a proxy for poverty at the school level, because Census poverty data is not available disaggregated below the school district level and is not collected annually.”).

217Nance, Students, Security, and Race, supra note 1, at 33.

218Id.; see also Kelly Welch & Allison Ann Payne, Racial Threat and Punitive School Discipline, 57 Soc. Probs. 25, 27 (2010) (“One factor presumed to be closely associated with school punitiveness and disciplinary practice is the level of school crime and disorder.”).

2192009–2010 SSOCS Questionnaire, supra note 210, at 11.

220See Nance, Students, Security, and Race, supra note 1, at 33; see also Kupchik & Ward, supra note 187, at 341.

221Violent incidents included robbery with or without a weapon, physical attack with or without a weapon, rape or attempted rape, and sexual battery other than rape. 2009–2010 SSOCS Questionnaire, supra note 210, at 11.

222Id.

223Id.

224Id.

225Id.

226Id.

227Nance, Students, Security, and Race, supra note 1, at 33–34; see also Kupchik & Ward, supra note 187, at 341–42; Welch & Payne, supra note 218, at 27.

2282009–2010 SSOCS Questionnaire, supra note 210, at 13. Principals responded whether these types of problems happened daily, at least once a week, at least once a month, on occasion, or never. Id. I created my index by recoding the scale so that the higher values indicated greater frequency and computed the mean value of the principals’ responses. Nance, Students, Security, and Race, supra note 1, at 34; see also Kupchik & Ward, supra note 187, at 341–42.

229Nance, Students, Security, and Race, supra note 1, at 34.

2302009–2010 SSOCS Questionnaire, supra note 210, at 17.

231Nance, Students, Security, and Race, supra note 1, at 34.

232Id. at 35.

233Id.

234Id.

235Id. I examined only secondary schools; accordingly, I controlled for whether the school was a middle school, defined as a “school[] in which the lowest grade is not lower than grade 4 and the highest grade is not higher than grade 9,” a high school, defined as a “school[] in which the lowest grade is not lower than grade 9 and the highest grade is not higher than grade 12,” or a combined school, defined as “other combinations of grades, including K–12 schools.” Samantha Neiman & Monica R. Hill, Crime, Violence, Discipline, and Safety in U.S. Public Schools: Findings from the School Survey on Crime and Safety: 2009–10, at 7 tbl.1 n.5 (2011), http://nces.ed.gov/pubs2011/2011320.pdf.

236Nance, Students, Security, and Race, supra note 1, at 35.

237Id. at 35–36.

238Id. at 40–41.

239Id.

240See Lucinda Gray, Laurie Lewis & John Ralph, Public School Safety and Discipline: 2013–14 (2015), http://nces.ed.gov/pubs2015/2015051.pdf.

241See, e.g., Stephanie Banchero & Caroline Porter, In Pursuit of Safety, Schools’ Paths Diverge, Wall St. J. (Jan. 17, 2013, 7:49 PM), http://online.wsj.com/article/SB10001424127887323783704578248113754373942.html (“Schools are running security drills, planning to install security cameras and bullet-proof glass, and hiring safety consultants.”); Lynh Bui, Public Gets First Chance to Weigh in on Proposed FY2014 Budget for Montgomery Co. Schools, Wash. Post (Jan. 11, 2013), http://www.washingtonpost.com/blogs/maryland-schools-insider/post/montgomery-county-parents-ask-for-more-school-security-teacher-training-during-budget-hearing/2013/01/11/e8d3dcf4-5aab-11e2-9fa9-5fbdc9530eb9_blog.html (discussing the increased concerns parents have for their children’s safety and their demand for increased on-campus security); Motoko Rich, School Officials Look Again at Security Measures Once Dismissed, N.Y. Times (Dec. 18, 2012), http://www.nytimes.com/2012/12/19/education/after-newtown-shootings-schools-consider-armed-security-officers.html?pagewanted=1&_r=1 (discussing the reactions of policymakers to the shootings at Sandy Hook Elementary School).

242E-mail from Nat’l Ctr. for Educ. Statistics to Jason P. Nance, Assoc. Professor of Law, Univ. of Fla. Levin Coll. of Law (May 21, 2013, 2:41 PM) (on file with author).

243Gray, Lewis & Ralph, supra note 240, at 1.

244NCES reported an unweighted survey response rate of 86%. Id.

245See supra note 210 and accompanying text for a description of “restricted-use data.”

246Gray, Lewis & Ralph, supra note 240, at C-4 to C-5. However, unlike the 2009–2010 SSOCS, which requested that the principal complete the survey, the 2013–2014 SSOCS requests that the “person(s) most knowledgeable about safety and discipline at [the] school” complete the survey. Id. at C-2.

247I created eight combinations of security measures: combination 1 includes metal detectors and SROs/guards; combination 2 includes metal detectors and random sweeps; combination 3 includes random sweeps and locked gates; combination 4 includes metal detectors and locked gates; combination 5 includes locked gates and SROs/guards; combination 6 includes random sweeps and SROs/guards; combination 7 includes metal detectors, SROs/guards, and random sweeps; and combination 8 includes metal detectors, locked gates, and SROs/guards. I included more combinations in the 2013–2014 SSOCS Study than in the 2009–2010 SSOCS Study to underscore the strong association between student race and strict security measures in a variety of contexts. However, unlike the 2009–2010 SSOCS Study, none of the combinations I included in the 2013–2014 SSOCS Study had more than three types of measures. The reason for the low number of measures is that the sample sizes in the 2013–2014 SSOCS Study were much smaller than the sample sizes in the 2009–2010 SSOCS Study, which significantly affected the statistical power of the analyses. That is, while several schools indicated that they relied on combinations of security measures greater than three (or other combinations of three not included in the study) and there were observable differences when I disaggregated the data by race, my empirical analyses did not have the statistical power to detect those racial differences to a statistically-significant degree.

248Creating dummy variables is a way to include categorical predictor variables in estimation models such as logistic regression. See Inst. for Dig. Research & Educ., FAQ: What is Dummy Coding?, UCLA, http://www.ats.ucla.edu/stat/mult_pkg/faq/general/dummy.htm (last visited July 26, 2016).

249Compare Gray, Lewis & Ralph, supra note 240, at C-6 (asking participants for information about five types of incidents), with 2009–2010 SSOCS Questionnaire, supra note 210, at 11 (asking participants for information about twelve types of incidents).

250This category included incidents such as rape, sexual battery other than rape, robbery with a weapon, physical attack with a weapon, and threat of physical attack with a weapon. Gray, Lewis & Ralph, supra note 240, at C-6.

251This category included incidents such as robbery without a weapon, physical attack without a weapon, and threat of physical attack without a weapon. Id. While I could have categorized incidents in other ways, I chose to categorize them in this manner because incidents involving weapons and sexual battery tend to be much more serious than the other listed offenses.

252See Kupchik & Ward, supra note 187, at 342 (transforming continuous variables by including their natural logs to correct for positive skewness); Nance, Students, Security, and Race, supra note 1, at 36.

253Gray, Lewis & Ralph, supra note 240, at C-6. Unlike the 2009–2010 SSOCS, the 2013–2014 SSOCS did not ask survey participants about gang activities or cult or extremist group activities at school. Compare id. (not asking participants for information about gang, cult, and extremist activities), with 2009–2010 SSOCS Questionnaire, supra note 210, at 12–13 (asking participants for information about gang, cult, and extremist activities).

254Unfortunately, unlike the 2009–2010 SSOCS, the 2013–2014 SSOCS did not provide information on student poverty; neighborhood crime; the percentage of students enrolled in special education; the percentage of students with limited English proficiency; the percentage of students who scored in the bottom 15% on state standardized exams; and daily attendance. Compare 2009–2010 SSOCS Questionnaire supra note 210, at 16–17 (asking participants for this information), with Gray, supra note 240, at C-2 to C-6 (not asking participants to submit this information).

255See Nance, Students, Security, and Race, supra note 1, at 35. I examined only secondary schools. Accordingly, I controlled for whether the school was a middle school, defined as a school “in which the lowest grade is greater than or equal to 4 and the highest grade is less than or equal to 9,” a high school, defined as a school “in which the lowest grade is greater than or equal to 9 and the highest grade is less than or equal to 12,” or a combined school, defined as a school “with all other combinations of grades, including K–12.” Gray, Lewis & Ralph, supra note 240, at 2 n.3. Unlike the 2009–2010 SSOCS, the 2013–2014 SSOCS merged high schools and combined schools into one category. See Neiman & Hill, supra note 235, at 1, 7 tbl.1 (listing combined schools and high schools as separate categories); see also Gray, Lewis & Ralph, supra note 240, at 2 n.3 (combining high schools and combined schools into one data category).

256The 2009–2010 SSOCS database provided a continuous variable for student population. See Nance, Students, Security, and Race, supra note 1, at 35.

257See generally Joseph F. Hair, Jr. et al., Multivariate Data Analysis 276–81 (5th ed. 1998) (providing an overview of logistic regression analysis).

258The weighted variable compensates for unequal probabilities of selection, minimizes bias associated with responding and nonresponding schools, reduces sampling error, and calibrates the data to known population characteristics to produce optimal national estimates. See Gray, Lewis & Ralph, supra note 240, at B-4 (describing the specific weighting procedures employed). Furthermore, I adjusted the sample weight by dividing it by its mean to create a mean weight of one. This is a recommended procedure when employing logistic regression analysis using SPSS. See Helen M. Marks & Jason P. Nance, Contexts of Accountability Under Systemic Reform: Implications for Principal Influence on Instruction and Supervision, 43 Educ. Admin. Q. 3, 14 (2007); Patty Glynn, Adjusting or Normalizing Weights “On the Fly” in SPSS, U. Wash., http://staff.washington.edu/glynn/adjspss.pdf (last updated July 8, 2004).

259An exponentiated coefficient estimates the change in odds of a school using a certain combination of security practices for each one-unit increase in an independent variable. See Raymond E. Wright, Logistic Regression, in Reading and Understanding Multivariate Statistics 217, 223 (Laurence G. Grimm & Paul R. Yarnold eds., 1995). For categorical variables, it represents a change in the odds of a school using a certain combination of security practices when that condition is present compared to the specified comparison variable noted. See id. at 233. For example, the change in odds of using a specific combination of security practices for schools with minority populations of between 0%–19% verses schools with minority populations of over 50%.

260The variance inflation factors (VIF), a common statistic to detect multicollinearity, indicated that multicollinearity was not a problem for the models. Additionally, the Hosmer & Lemeshow test, a statistical test to assess a lack of fit between the data and the model, indicated a goodness of fit for each of the eight models except for the combination of measures that included random sweeps and locked gates (combination 3).

261When an exponentiated coefficient is less than one, the odds ratio is easier to interpret by dividing one by the exponentiated coefficient. See Michael H. Katz, Multivariable Analysis: A Practical Guide for Clinicians 130 (1999) (explaining a common procedure for computing an odds ratio for exponentiated coefficients less than one to facilitate interpretation). Following this procedure, the odds of relying on each combination of security measures for schools with minority populations of over 50% compared to schools serving minority populations of between 0% and 19% are as follows: 5.8 (combination 1); 2.8 (combination 2); 2.9 (combination 3); 18.1 (combination 4); 3.4 (combination 5); 2.0 (combination 6); 2.2 (combination 7); 15.1 (combination 8).

262Specifically, the odds of relying on each combination of security measures for schools with minority populations of over 50% compared to schools serving minority populations of between 20% and 49% are as follows: 3.2 (combination 1); 2.7 (combination 2); 2.3 (combination 3); 3.3 (combination 4); 2.0 (combination 5); 1.6 (combination 6); 2.3 (combination 7); 3.0 (combination 8).

263See Finn & Servoss, supra note 37, at 46; Servoss & Finn, supra note 141, at 68–69.

264Finn & Servoss, supra note 37, at 49; see also Servoss & Finn, supra note 141, at 79–80 (“In sum, a high proportion of Black students in a school is related to the degree of security the school implements above and beyond all other characteristics we studied.”).

265Finn & Servoss, supra note 37, at 44, 49; see also Servoss & Finn, supra note 141, at 79–80.

266Servoss & Finn, supra note 141, at 80. In another study that Timothy Servoss conducted using the Education Longitudinal Study of 2002, he found that “students in high security schools are 11.78 times more likely to be African American than White . . . , and 1.56 times more likely to be Hispanic/Latino than White.” Servoss, supra note 128, at 13; see also Ivory A. Toldson, Breaking Barriers 2: Plotting the Path Away from Juvenile Detention and Toward Academic Success for School-Age African American Males 7 (2011), http://cbcfinc.org/oUploadedFiles/BreakingBarriers2.pdf (finding that black students were approximately 4.8 times more likely to report passing through a metal detector when entering school than white students, and Latino students were approximately 2.65 times as likely to report passing through metal detectors when entering school than white students); see also Thomas J. Mowen & Karen F. Parker, Minority Threat and School Security: Assessing the Impact of Black and Hispanic Student Representation on School Security Measures, Security J. 1 (2014) (finding that the percentage of African-American students at a school was positively related to the use of strict security measures).

267Karen J. DeAngelis et al., The Hidden Cost of School Security, 36 J. Educ. Fin. 312 (2011).

268Id. at 318–19.

269Id. at 329.

270Id. at 329–31.

271Welch & Payne, supra note 218, at 36.

272Id.

273Id.

274Russell J. Skiba, Mariella I. Arredondo & Natasha T. Williams, More Than a Metaphor: The Contribution of Exclusionary Discipline to a School-to-Prison Pipeline, 47 Equity & Excellence Educ. 546, 550 (2014); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 331–32; Nance, Students, Police, and the School-to-Prison Pipeline, supra note 1, at 957.

275See U.S. Dep’t of Educ. Office for Civil Rights, Civil Rights Data Collection, Data Snapshot: School Discipline 2, 6 (2014), [hereinafter Data Snapshot]; see also Losen & Gillespie, supra note 184, at 6 (observing that one out of every six African-American students enrolled in K–12 schools had been suspended at least once, but only one out of twenty white students had been suspended).

276Data Snapshot, supra note 275, at 1. According to highlights of the 2013–2014 CRD Collection that the OCR released in June 2016, significant racial disparities still exist relating to school discipline. Office for Civil Rights, U.S. Dep’t of Educ., 2013–2014 Civil Rights Data Collection: A First Look 3 (2016). According to that data, African-American students are approximately 3.8 times more likely to receive one or more out-of-school suspensions than white students. Id. Further, although African-American children represented 19% of students enrolled in pre-school, they represented 47% of students receiving one or more out-of-school suspensions. Id.

277See Dear Colleague Letter, supra note 4, at 4.

278Id.

279See, e.g., Losen, supra note 184, at 6–7; Catherine P. Bradshaw et al., Multilevel Exploration of Factors Contributing to the Overrepresentation of Black Students in Office Disciplinary Referrals, 102 J. Educ. Psychol. 508, 513–14 (2010) (observing that, after controlling for teacher ratings of students’ behavior problems, African-American students were more likely than white students to be referred to the office for disciplinary reasons); Theresa Glennon, Looking for Air: Excavating Destructive Educational and Racial Policies to Build Successful School Communities, in Justice for Kids: Keeping Kids Out of the Juvenile Justice System 107, 110–11 (Nancy E. Dowd ed., 2011) (discussing studies demonstrating that minority students are disciplined disproportionately); Anna C. McFadden et al., A Study of Race and Gender Bias in the Punishment of Handicapped School Children, 24 Urb. Rev. 239, 246–47 (1992) (finding that African-American male disabled students were punished more severely than other students for the same offenses); Michael Rocque & Raymond Paternoster, Understanding the Antecedents of the “School-to-Jail” Link: The Relationship Between Race and School Discipline, 101 J. Crim. L. & Criminology 633, 651–54 (2011) (concluding that African-American students are significantly more likely than white students to be disciplined even after taking into account other salient factors such as grades, attitudes, gender, special education or language programs, and their conduct in school as perceived by teachers); Russell J. Skiba et al., Race Is Not Neutral: A National Investigation of African American and Latino Disproportionality in School Discipline, 40 Sch. Psychol. Rev. 85, 95–101 (2011) (observing significant racial disparities in school discipline after examining an extensive national sample); Russell J. Skiba et al., Where Should We Intervene? Contributions of Behavior, Student, and School Characteristics to Out-of-School Suspension, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 132, 132–35 (Daniel J. Losen ed., 2015) (finding that race was a strong predictor of out-of-school suspensions); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 332.

280Russell J. Skiba & Natasha T. Williams, Are Black Kids Worse? Myths and Facts About Racial Differences in Behavior 3 (2014).

281Catherine Y. Kim, Daniel J. Losen & Damon T. Hewitt, The School-to-Prison Pipeline: Structuring Legal Reform 53–54 (2010); Pedro A. Noguera, The Trouble with Black Boys and Other Reflections on Race, Equity, and the Future of Public Education, at xvii (2008); Nancy E. Dowd, What Men?: The Essentialist Error of the “End of Men”, 93 B.U. L. Rev. 1205, 1216–22 (2013).

282Angela A. Ciolfi & James E. Ryan, Race and Response-to-Intervention in Special Education, 54 How. L.J. 303, 326–27 (2011).

283See, e.g., Susan Aud, Mary Ann Fox & Angelina KewalRamani, Status and Trends in the Education of Racial and Ethnic Groups, 92 tbl.17a (2010); Catherine E. Lhamon, Five New Facts from the Civil Rights Data Collection, U.S. Dep’t Educ.: Homeroom (Mar. 21, 2014), http://www.ed.gov/blog/2014/03/five-new-facts-from-the-civil-rights-data-collection/.

284See, e.g., Sarah E. Redfield, Diversity Realized: Putting the Walk with the Talk for Diversity in the Legal Profession 72–76 (2009); Ronald F. Ferguson, Teachers’ Perceptions and Expectations and the Black-White Test Score Gap, 38 Urb. Educ. 460, 477–78 (2003).

285And, of course, they do not even scratch the surface of the inequalities present in our nation with respect to minorities generally relative to interactions with the police, our justice system, and employment, just to name a few. See, e.g., Deborah L. Rhode, From Platitudes to Priorities: Diversity and Gender Equity in Law Firms, 24 Geo. J. Legal Ethics 1041, 1045–46 (2011) (discussing racial inequalities for minorities in legal jobs); L. Song Richardson, Arrest Efficiency and the Fourth Amendment, 95 Minn. L. Rev. 2035, 2036–38 (2011) (providing evidence that demonstrates that although stops and searches of whites proportionately yield more illegal contraband, the police still stop and search African-Americans at higher rates); see generally Michelle Alexander, The New Jim Crow: Mass Incarceration in the Age of Colorblindness (2010) (discussing racial inequalities present throughout the justice system); Paul Butler, One Hundred Years of Race and Crime, 100 J. Crim. L. & Criminology 1043, 1045–46 (2010) (describing racial inequalities present throughout American society).

286Nat’l Ctr. for Juvenile Justice, Juvenile Arrest Rates by Offense, Sex, and Race: 1980–2011 (2014), http://www.ojjdp.gov/ojstatbb/crime/excel/JAR_2011.xls; see also Dowd, supra note 281, at 1222–26 (providing evidence that African-American youth are overrepresented in the juvenile justice system and receive disparate and harsher treatment).

287Mark Soler, Dana Shoenberg & Marc Schindler, Juvenile Justice: Lessons for a New Era, 16 Geo. J. on Poverty L. & Pol’y 483, 530–31 (2009).

288See Office of Juvenile Justice & Delinquency Prevention, Juvenile Offenders and Victims: 2014 National Report 163–64 (Melissa Sickmund & Charles Puzzanchera eds., 2014). The greatest disparity was for drug offenses, where African-Americans were almost twice as likely to be detained in drug cases as whites. Id. at 164.

289See supra Part III.

290I emphasize here that these empirical studies do not affirmatively establish that student race causes school officials to implement tighter security measures. These empirical studies only demonstrate a strong relationship between race and the decisions to implement strict security measures. More research must be conducted to establish causation.

291See Nance, Over-Disciplining Students, supra note 1, at 1067; Redfield, supra note 31.

292Nance, Over-Disciplining Students, supra note 1, at 1067; Redfield, supra note 31; see also Cheryl Staats, Understanding Implicit Bias: What Educators Should Know, Am. Educ., Winter 2015–2016, at 29, 29, http://www.aft.org/sites/default/files/ae_winter2015staats.pdf (“As a profession, teaching is full of well-intentioned individuals deeply committed to seeing all children succeed.”); cf. L. Song Richardson, Police Efficiency and the Fourth Amendment, 87 Ind. L.J. 1143, 1148 (2012) (“The typical arguments that the disproportionate policing of Blacks can be explained either by conscious racial bias on the part of the police or by the assumption that Blacks engage in more ambiguously criminal behavior does not withstand scrutiny.”).

293See, e.g., Daniel J. Losen et al., Disturbing Inequities: Exploring the Relationship Between Racial Disparities in Special Education Identification and Discipline, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 89, 91–92 (Daniel J. Losen ed., 2015) (explaining that minority students are more likely to have inexperienced teachers).

294See, e.g., Advancement Project, Power in Partnerships: Building Connections at the Intersection of Racial Justice and LGBTQ Movements to End the School-to-Prison Pipeline 5 (2015), http://b.3cdn.net/advancement/85066c4a18d249e72b_r23m68j37.pdf (“Implicit bias also plays a role in funneling Black, Brown, and LGBTQ students into the school-to-prison pipeline.”); Derek W. Black, Education Law: Equality, Fairness, and Reform 147 (2013) (“[T]oday racial discrimination is more likely to be the result of subtle or unconscious biases, on which a state actor may not even realize it is acting.”); Johanna Wald, Can “De-Biasing” Strategies Help to Reduce Racial Disparities in School Discipline?: Summary of the Literature 1–3 (2014), http://www.indiana.edu/~atlantic/wp-content/uploads/2014/03/Implicit-Bias_031214.pdf (arguing that the empirical evidence suggests that implicit racial bias contributes to differential treatment of racial minorities in schools); Jamilia J. Blake, Bettie Ray Butler & Danielle Smith, Challenging Middle-Class Notions of Femininity: The Causes of Black Females’ Disproportionate Suspension Rates, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 75, 76 (Daniel J. Losen ed., 2015) (“Although a number of factors are believed to contribute to disproportionate disciplinary practices, racial/ethnic bias has been implicated most frequently.”); Pamela Fenning & Jennifer Rose, Overrepresentation of African American Students in Exclusionary Discipline: The Role of School Policy, 42 Urb. Educ. 536, 537 (2007) (arguing that students of color are targeted by teachers out of fear and anxiety of losing control of the classroom); Kent McIntosh et al., Education Not Incarceration: A Conceptual Model for Reducing Racial and Ethnic Disproportionality in School Discipline, 5 J. Applied Res. on Child. 6–7 (2014), http://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=1215&context=childrenatrisk (maintaining that conscious or unconscious bias is an important factor in the discipline gap); see also Nance, Over-Disciplining Students, supra note 1, at 1067–68; cf. Gary Blasi, Advocacy Against the Stereotype: Lessons from Cognitive Social Psychology, 49 UCLA L. Rev. 1241, 1276 (2002) (maintaining that Americans’ behavior is determined to some degree by unconscious racial biases); Jerry Kang, Trojan Horses of Race, 118 Harv. L. Rev. 1489, 1512–17 (2005) (discussing social cognition research that shows that most people hold implicit biases against racial minorities); Cynthia Lee, Making Race Salient: Trayvon Martin and Implicit Bias in a Not Yet Post-Racial Society, 91 N.C. L. Rev. 1555, 1570 (2013) (“Despite our largely egalitarian attitudes and beliefs, social science research over the past decade has shown that a majority of Americans are implicitly biased against Blacks.”); Jeffery J. Rachlinski et al., Does Unconscious Racial Bias Affect Trial Judges?, 84 Notre Dame L. Rev. 1195, 1196–97 (2009) (“Implicit bias . . . also appears to be an important source of racial disparities in the criminal justice system.”).

295See, e.g., Daniel Kahneman, Thinking, Fast and Slow (2011); Ziva Kunda, Social Cognition: Making Sense of People (1999); Nilanjana Dasgupta, Implicit Ingroup Favoritism, Outgroup Favoritism, and Their Behavioral Manifestations, 17 Soc. Just. Res. 143, 144–46 (2004) (describing the development of implicit bias theory); Jonathan St. B.T. Evans & Keith E. Stanovich, Dual-Process Theories of Higher Cognition: Advancing the Debate, 8 Persp. on Psych. Sci. 223 (2013); Matthew D. Lieberman, Reflexive and Reflective Judgment Processes: A Social Cognition Neuroscience Approach, in Social Judgments: Implicit and Explicit Processes 44 (Joseph P. Forgas, Kipling D. Williams & William Von Hippel eds., 2003); Eliot R. Smith & Jamie DeCoster, Dual-Process Models in Social and Cognitive Psychology: Conceptual Integration and Links to Underlying Memory Systems, 4 Personality & Soc. Psych. Rev. 108 (2000).

296Dasgupta, supra note 295.

297See id. at 144–46; see also Darren Lenard Hutchinson, “Continually Reminded of Their Inferior Position”: Social Dominance, Implicit Bias, Criminality, and Race, 46 Wash. U. J.L. & Pol’y 23, 35 (2014); Richardson, supra note 292, at 1146–47.

298See Kunda, supra note 295, at 266; Anthony G. Greenwald & Linda Hamilton Krieger, Implicit Bias: Scientific Foundations, 94 Calif. L. Rev. 945, 946 (2006); Hutchinson, supra note 297, at 35; Kristin A. Lane, Jerry Kang & Mahzarin R. Banaji, Implicit Social Cognition and Law, 3 Ann. Rev. L. Soc. Sci. 427, 428 (2007); Richardson, supra note 292, at 1146–47.

299See Greenwald & Krieger, supra note 298, at 946; Richardson, supra note 292, at 1147.

300Staats, supra note 292, at 29; see also Kahneman, supra note 295.

301See Kahneman, supra note 295, at 20–21. Other scientists and researchers also have described the dual system of information processing, see, e.g., Evans & Stanovich, supra note 295, and some of them have referred to the dual system of information processing using other terms, see, e.g., Lieberman, supra note 295 at 46–47 (describing reflexive processes and reflective processes). However, not all dual process theories are alike. See Evan & Stanovich, supra note 295, at 226–27. Further, scientists are still researching whether there are indeed two systems, more than two systems, or simply one system with multiple processes. See Pamela Casey, Kevin Burke & Steve Leben, Minding the Court: Enhancing the Decision-Making Process 5 n.6 (2012).

302Kahneman, supra note 295, at 21.

303See id. (“System 2 allocates attention to the effortful mental activities that demand it, including complex computations. The operations of System 2 are often associated with the subjective experience of agency, choice, and concentration.”); Evans & Stanovich, supra note 295, at 223–25; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365.

304See Kahneman, supra note 295, at 20 (“System 1 operates automatically and quickly, with little or no effort and no sense of voluntary control.”); Evans & Stanovich, supra note 295, at 223–25; McIntosh et al., supra note 294, at 5; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365–66.

305See Kahneman, supra note 295, at 20; Evans & Stanovich, supra note 295, at 223–25; McIntosh et al., supra note 294, at 5; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365–66.

306Casey, Burke & Leben, supra note 301, at 5–6; Kang, supra note 294, at 1499; McIntosh et al., supra note 294, at 5; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 366.

307See Kang, supra note 294, at 1498–99.

308Id. (quoting Susan T. Fiske & Shelley E. Taylor, Social Cognition (2d ed. 1991)).

309See Jerry Kang, Implicit Bias: A Primer for Courts 1 (2009).

310See Kang, supra note 294, at 1498–99.

311See id. at 1499; Richardson, supra note 285, at 2042.

312See Staats, supra note 292, at 32.

313Susan T. Fiske & Steven L. Neuberg, A Continuum of Impression Formation, from Category-Based to Individuating Processes: Influences of Information and Motivation on Attention and Interpretation, 23 Advances Experimental Soc. Psychol. 1, 23–24 (1990); Richardson, supra note 285, at 2042.

314See Elise C. Boddie, Racial Territoriality, 58 UCLA L. Rev. 401, 439–40 (2010); Greenwald & Krieger, supra note 298, at 948–50; Richardson, supra note 292, at 1147.

315Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364; Jerry Kang et al., Implicit Bias in the Courtroom, 59 UCLA L. Rev. 1124, 1128 (2012); Nance, Over-Disciplining Students, supra note 1, at 1068–69.

316Hutchinson, supra note 297, at 35; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364; Nance, Over-Disciplining Students, supra note 1, at 1069.

317Kang et al., supra note 315, at 1128; see also Greenwald & Krieger, supra note 298, at 949; Hutchinson, supra note 297, at 36; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364; Nance, Over-Disciplining Students, supra note 1, at 1069; Richardson, supra note 292, at 1147.

318Kang et al., supra note 315, at 1128–29; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364.

319Kang et al., supra note 315, at 1129; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 364.

320Supra note 319.

321See Anthony G. Greenwald et al., Understanding and Using Implicit Association Test: III. Meta-Analysis of Predictive Value, 97 J. Personality & Soc. Psychol. 17, 18–19 (2009) (discussing the multitude of studies that have confirmed the validity of the IAT); Hutchinson, supra note 297, at 38–39; Kang, supra note 294, at 1509 (“The Implicit Association Test (IAT) has become the state-of-the-art measurement tool.”); Kristen A. Lane et al., Understanding and Using the Implicit Association Test: IV: What We Know (So Far) About the Method, in Implicit Measures of Attitudes 59, 65 (Bernd Wittenbrink & Norbert Schwarz eds., 2007) (discussing how the IAT test has been employed in social cognition studies, clinical studies, health studies, marketing studies, legal studies, and to measures attitudes toward death, nature, celebrities, foods, cities, geography, public opinion issues, and politics); Rachlinski et al., supra note 294, at 1198. However, after reviewing the literature discussing the predictive value of the IAT, Marianne Bertrand and Esther Duflo caution that the IAT should not “be considered a ‘magic bullet,’ suitable to replace any other measure of discrimination.” Marianne Bertrand & Esther Duflo, Field Experiments on Discrimination 30–34 (Nat’l Bureau of Econ. Research, Working Paper No. 22014, 2016). Nevertheless, they conclude that the IAT “can be an extremely useful intermediate variable[] to understand the mechanisms beyond a result or potentially, if collected beforehand, a covariate of interest.” Id. at 34 (citation omitted).

322See Mahzarin R. Banaji & Anthony G. Greenwald, Blind Spot: Hidden Biases of Good People 32–52 (2013); Rachlinski et al., supra note 294, at 1198.

323See Banaji & Greenwald, supra note 322, at 39; Greenwald & Krieger, supra note 298, at 952.

324Greenwald & Krieger, supra note 298, at 952. Other IAT tests measure biases with respect to Native Americans, age, disability, sexuality, gender, weight, Asians, skin-tone, and Arab-Muslims, among others. See Project Implicit: Take a Test, Harv. Univ., https://implicit.harvard.edu/implicit/selectatest.html (last visited Feb. 9, 2016).

325Project Implicit: Take a Test, supra note 324.

326Id.

327Id.

328Id.

329Id.

330Id.; see also Banaji & Greenwald, supra note 322, at 42.

331Banaji & Greenwald, supra note 322, at 47.

332See Project Implicit: Take a Test, supra note 324.

333Banaji & Greenwald, supra note 322, at 47; see also Brian A. Nosek, Mahzarin R. Banaji & Anthony G. Greenwald, Harvesting Implicit Group Attitudes and Beliefs from a Demonstration Web Site, 6 Group Dynamics 101, 105 (2002) (providing data that most test-takers of the Race IAT show an implicit white preference); Rachlinski et al., supra note 294, at 1199.

334Rachlinski et al., supra note 294, at 1199. Notably, African-Americans demonstrate different patterns of preference. Id. Overall, they exhibit a slight implicit preference for whites, but there is a wide variety in their responses, with some African-Americans expressing moderate to strong preferences for African-Americans, a preference rarely exhibited by whites. Id. at 1199–2000.

335See Hal R. Arkes & Philip E. Tetlock, Attributions of Implicit Prejudice, or “Would Jesse Jackson ‘Fail’ the Implicit Association Test?”, 15 Psychol. Inquiry 257 (2004); Gregory Mitchell & Philip E. Tetlock, Antidiscrimination Law and the Perils of Mindreading, 67 Ohio St. L.J. 1023 (2006); Frederick L. Oswald et al., Predicting Ethnic and Racial Discrimination: A Meta-Analysis of IAT Criterion Studies, 105 J. Personality & Soc. Psychol. 171 (2013); see also Bertrand & Duflo, supra note 321, at 32–33 (discussing studies questioning the predictive value of the IAT).

336Banaji & Greenwald, supra note 322, at 47.

337See Allen R. McConnell & Jill M. Leibold, Relations Among the Implicit Association Test, Discriminatory Behavior, and Explicit Measures of Racial Attitudes, 37 J. Experimental Soc. Psychol. 435, 440–42 (2001) (finding that those that tested for stronger negative attitudes towards African-Americans on the IAT exhibited more negative social interactions with African-American interviewers); Jonathan C. Ziegert & Paul J. Hanges, Employment Discrimination: The Role of Implicit Attitudes, Motivation, and a Climate for Racial Bias, 90 J. Applied Psychol. 553, 560–61 (2005) (empirically finding that implicit racism as measured by the Race IAT predicted racially biased discriminatory actions).

338See Alexander R. Green et al., Implicit Bias Among Physicians and Its Prediction of Thrombolysis Decisions for Black and White Patients, 22 J. Gen. Internal Med. 1231, 1235–37 (2007) (finding empirically that physicians’ self-reported explicit biases did not influence decisions for optimal medical treatment, but their implicit biases, as measured by the Race IAT, did influence those decisions).

339See Kurt Hugenberg & Galen V. Bodenhausen, Facing Prejudice: Implicit Prejudice and the Perception of Facial Threat, 14 Psychol. Sci. 640, 643 (2003) (finding empirically that participants with higher implicit biases as measured by the Race IAT “saw hostility as lingering longer and appearing more quickly on the faces of African Americans” compared to participants who had lower implicit biases).

340See Anthony Greenwald et al., supra note 321.

341See Banaji & Greenwald, supra note 322, at 49 (“The meta-analysis answered the most important question about which we had been uncertain in the first several years of the IAT’s existence: It clearly showed that the Race IAT predicted racially discriminatory behavior.”). See generally Greenwald et al., supra note 321. Further, research has demonstrated that the results from the IAT test are not determined by participants’ handedness, the order in which participants perform the requested tasks, or another artifact of the experimental design. See Anthony G. Greenwald & Brian A. Nosek, Health of the Implicit Association Test at Age 3, 48 Zeitschrift für Experimentelle Psychologie 85, 87 (2001) (showing that handedness was not related to the Race IAT results); Anthony G. Greenwald, Brian A. Nosek & Mahzarin R. Banaji, Understanding and Using Implicit Association Test: I. An Improved Scoring Algorithm, 85 J. Personality & Soc. Psychol. 197, 209–11 (2003) (making adjustments to reduce order effect); Rachlinski et al., supra note 294, at 1200–01. But see Oswald et al., supra note 335, at 182–83 (finding that IATs were poor predictors of a wide range of discrimination measures except for brain activity).

342Rachlinski et al., supra note 294, at 1201 (footnotes omitted); see also Bertrand & Duflo, supra note 321, at 34 (discussing the utility of the IAT after reviewing the literature discussing the predictive value of the IAT).

343See Hutchinson, supra note 316, at 37; Rachlinski et al., supra note 294, at 1197; Richardson, supra note 285, at 2043.

344Casey, Burke & Levin, supra note 301, at 10; Kang, supra note 294, at 1514; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 365; Nance, Over-Disciplining Students, supra note 1, at 1069; Richardson, supra note 285, at 2043.

345See Richardson, supra note 292, at 1147; Staats, supra note 292, at 31; see also Jennifer L. Eberhardt et al., Seeing Black: Race, Crime, and Visual Processing, J. Personality & Soc. Psychol. 876, 876 (2004) (“The stereotype of Black Americans as violent and criminal has been documented by social psychologists for almost 60 years.”); Katheryn Russell-Brown, Black Men and the Police: Making Implicit Bias Explicit, in Policing Black Men: Arrest, Prosecution and Prison (Angela J. Davis ed., forthcoming) (manuscript at 11) (“Overall, [the four studies] demonstrate that the sledge hammer of racial bias will have a disproportionate impact on those deemed ‘most’ Black.”); Sophie Trawalter et al., Attending to Threat: Race-Based Patterns of Selective Attention, 44 J. Experimental Soc. Psychol. 1322, 1326–27 (2008).

346B. Keith Payne, Prejudice and Perception: The Role of Automatic and Controlled Processes in Misperceiving a Weapon, 81 J. Personality & Soc. Psychol. 181 (2001).

347Id. at 185.

348Id.

349Id. at 188.

350Joshua Correll et al., The Police Officer’s Dilemma: Using Ethnicity to Disambiguate Potentially Threatening Individuals, 83 J. Personality & Soc. Psychol. 1314, 1315 (2002).

351Id. at 1315, 1318, 1321.

352Id. at 1317.

353Id. at 1319.

354Id.

355Id. at 1321.

356The researchers define “shooter bias” as “faster responses to unarmed White than to unarmed African American targets, and to armed African American than armed White targets.” Id. at 1322.

357Id. at 1322–24.

358Id. at 1324.

359Id.

360John A. Bargh, Mark Chen & Lara Burrows, Automaticity of Social Behavior: Direct Effects of Trait Construct and Stereotype Activation on Action, 71 J. Personality & Soc. Psychol. 230, 238 (1996).

361Id.

362Id.

363Id.

364Id. at 239.

365Id.

366Id.

367See Sandra Graham & Brian S. Lowery, Priming Unconscious Racial Stereotypes About Adolescent Offenders, 28 Law & Hum. Behav. 483, 487 (2004).

368Those words included Harlem, homeboy, dreadlocks, basketball, minority, black, and afro, among others. Id. at 489 n.5.

369Those words included baby, enjoyment, heaven, kindness, summer, sunset, truth, devil, accident, funeral, horror, mosquito, stress, and toothache, among others. Id.

370Id. at 487.

371Id. at 487, 496.

372Id. at 494.

373Id. at 494, 497.

374Id. at 499.

375Franklin D. Gilliam, Jr. & Shanto Iyengar, Prime Suspects: The Influence of Local Television News on the Viewing Public, 44 Am. J. Pol. Sci. 560, 563 (2000).

376Id.

377Id. at 567–68.

378See, e.g., Birt L. Duncan, Differential Social Perception and Attribution of Intergroup Violence: Testing the Lower Limits of Stereotyping of Blacks, 34 J. Personality & Soc. Psychol. 590, 595 (1976) (finding that when, following a script, one white man shoved another white man, only 13% of viewers thought that this act was aggressive; but when both men were black, following the same script, that number rose to 69%); Eberhardt et al., supra note 345 (showing that participants who were subliminally primed with crime-relevant objects induced attentional biases towards African-American faces); Kurt Hugenberg & Galen V. Bodenhausen, Ambiguity in Social Categorization: The Role of Prejudice and Facial Affect in Race Categorization, 15 Psychol. Sci. 342 (2004) (demonstrating that implicit bias towards African-Americans, not explicit bias, was more strongly related with a tendency to categorize racially ambiguous faces of African-Americans as hostile).

379See, e.g., Joshua Correll et al., Across the Thin Blue Line: Police Officers and Racial Bias in the Decision to Shoot, 92 J. Personality & Soc. Psychol. 1006, 1020–22 (2007) (finding empirically when the targets were African-Americans, police officers manifested “robust racial bias in the speed with which they made shoot/don’t-shoot decisions”).

380See Green et al., supra note 338, at 1235–37; see also Janice A. Sabin et al., Physicians’ Implicit and Explicit Attitudes About Race by MD Race, Ethnicity, and Gender, 20 J. Health Care for Poor & Underserved 896, 901 (2009) (demonstrating empirically that the medical doctors participating in the study exhibited an overall strong implicit preference for whites over African-Americans).

381See Lee, supra note 294, at 1559; Rachlinski et al., supra note 294, at 1210 (finding empirically “strong white preference among the white judges”).

382See Banaji & Greenwald, supra note 322, at 47.

383Linda van den Bergh et al., The Implicit Prejudiced Attitudes of Teachers: Relations to Teacher Expectations and the Ethnic Achievement Gap, 47 Am. Educ. Res. J. 497, 504 (2010). The ethnic minority students were of Turkish and Moroccan origin; the majority students were of Dutch origin. Id.

384Id.

385Id. at 512.

386Id. at 514, 518.

387Id. at 519; see also Cheryl Staats, Kirwin Inst. for the Study of Race & Ethnicity, State of the Science: Implicit Bias Review 30–34 (2013); Clark McKown & Rhona S. Weinstein, Modeling the Role of Child Ethnicity and Gender in Children’s Differential Response to Teacher Expectations, 32 J. Applied Soc. Psychol. 159, 174–80 (2002) (finding that race predicts teacher expectancy effects that may exacerbate racial achievement gaps); Clark McKown & Rhona S. Weinstein, Teacher Expectations, Classroom Context, and the Achievement Gap, 46 J. Sch. Psychol. 235, 256 (2008) (showing empirically that teachers with high biases towards minority students experienced higher gaps in student achievement along racial lines than teachers with lower biases); Harriet R. Tenenbaum & Martin D. Ruck, Are Teachers’ Expectations Different for Racial Minority than for European American Students? A Meta-Analysis, 99 J. Educ. Psychol. 253, 271 (2007) (demonstrating that teachers have higher expectations for white students than for minority students, and that teacher expectancies may lead to differences in academic performances).

388Jason A. Okonofua & Jennifer L. Eberhardt, Two Strikes: Race and the Disciplining of Young Students, 26 Psychol. Sci. 617, 617 (2015).

389Id. at 618. The researchers counterbalanced the order in which they displayed the infractions across participants. Id.

390Id.

391Id.

392Id.

393Id. at 619.

394Id.

395Id. at 621; see also Clifton A. Casteel, Teacher-Student Interactions and Race in Integrated Classrooms, 92 J. Educ. Res., Nov./Dec. 1998, at 115, 119 (finding empirically that African-American students had more negative interactions with white teachers than white students, and white students had more positive interactions with teachers than African-American students); cf. Justin D. Levinson, Forgotten Racial Equality: Implicit Bias, Decisionmaking, and Misremembering, 57 Duke L.J. 345, 398–406 (2007) (demonstrating empirically that participants remembered more facts “relating to the actor’s aggressive actions” when listening to a story about “Tyronne” than listening to the same story about “William”).

396See supra notes 271–80 and accompanying text.

397See supra Part III.

398See Robert J. Sampson & Stephen W. Raudenbush, Seeing Disorder: Neighborhood Stigma and the Social Construction of “Broken Windows”, 67 Soc. Psychol. Q. 319 (2004).

399Id. at 324.

400Id. at 322–24.

401Id. at 324–27.

402Id. at 330, 336.

403Id. at 336.

404Id.

405See Lincoln Quillian & Devah Pager, Black Neighbors, Higher Crime? The Role of Racial Stereotypes in Evaluations of Neighborhood Crime, 107 Am. J. Soc. 717 (2001).

406Id. at 736–37, 741, 743, 747.

407Id. at 748.

408Correll et al., supra note 379.

409Id. at 1015.

410Richardson, supra note 292, at 1160; cf. Servoss, supra note 128, at 18 (finding that teachers in high security schools rated African-American students as more disruptive relative to their white peers and suggesting that teachers’ biases may be exacerbated against African-American students in higher security environments).

411See Servoss & Finn, supra note 141, at 64 (explaining that as the proportion of minorities in a school rises, school officials are more prone to rely on punitive discipline methods to combat crime-related threats and to maintain dominance).

412See supra Part II.

413See supra Part III.

414See Nance, Students, Security, and Race, supra note 1, at 48–55; see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 345–60.

415See supra note 414.

416See Nance, Students, Security, and Race, supra note 1, at 55.

417See id. Importantly, the U.S. Department of Education’s Office for Civil Rights recently recommended that schools use these approaches to create safe school climates. See Guiding Principles, supra note 153, at 5–7; Dear Colleague Letter, supra note 4, at app. 2.

418See U.S. Dep’t of Educ., State and Local Expenditures on Corrections and Education: A Brief from the U.S. Department of education, Policy and Program Studies Service 1 (2016). In addition to improving the quality of life for thousands of students, multiple empirical studies demonstrate the economic savings and benefits our nation could achieve by investing more in public education. See id. at 2–3.

419Courts also have a role to play. In my prior work, I proposed a reformulated legal framework to evaluate students’ Fourth Amendment rights in schools. See Nance, School Surveillance and the Fourth Amendment, supra note 1, at 83–84. In an upcoming article, I will propose another legal framework for courts to evaluate the constitutionality of coercive surveillance methods, one that will address the effects of implicit racial bias and help rectify the disproportionate application of strict security measures on students of color.

420See Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 362–71.

421See Nance, Over-Disciplining Students, supra note 1, at 1072.

422See Staats, supra note 387, at 53; Irene V. Blair, The Malleability of Automatic Stereotypes and Prejudice, 6 J. Personality & Soc. Psychol. Rev. 242, 247–48 (2002); Nilanjana Dasgupta & Anthony G. Greenwald, On the Malleability of Automatic Attitudes: Combating Automatic Prejudice with Images of Admired Disliked Individuals, 81 J. Personality & Soc. Psychol. 800, 808 (2001); Patricia G. Devine, Stereotypes and Prejudice: Their Automatic and Controlled Components, 56 J. Personality & Soc. Psychol. 5, 15–16 (1989); Erik J. Girvan, On Using the Psychological Science of Implicit Bias to Advance Anti-Discrimination Law, 26 Geo. Mason U. C.R.L.J. 1, 78 (2015); see also Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 367.

423See Nance, Over-Disciplining Students, supra note 1, at 1072.

424See Julia Edwards, Justice Dept. Mandates ‘Implicit Bias’ Training for Agents, Lawyers, Reuters (June 27, 2016, 9:14 PM), www.reuters.com/articles/us-usa-justice-bias-exclusive-idUSKCN0ZD251.

425Id.

426Id.; see also Russell-Brown, supra note 345.

427Implicit Bias Initiative, Am. Bar Ass’n, http://www.americanbar.org/groups/litigation/initiatives/task-force-implicit-bias.html (last visited July 14, 2016).

428Girvan, supra note 422, at 78; Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 367.

429See Banaji & Greenwald, supra note 322, at 152.

430Cheryl Staats, Implicit Bias, Intergroup Contact, and Debiasing: Considering Neighborhood Dynamics, N.Y.U. Furman Ctr. (Aug. 2014), http://furmancenter.org/research/iri/essay/implicit-bias-intergroup-contact-and-debiasing-considering-neighborhood-dyn (explaining that “[d]ebiasing requires consistent effort to modify existing mental associations”).

431See Nance, Persisting Inequalities, supra note 3 (discussing resource inequalities among schools).

432See supra note 103; see also Natasha Ushomirsky & David Williams, Funding Gaps 2015: Too Many States Still Spend Less on Educating Students Who Need the Most 1 (2015) (finding that school districts serving the highest concentrations of minority students on average spend $2000 less per student than school districts serving the highest concentrations of white students).

433See Nance, Over-Disciplining Students, supra note 1, at 1072.

434See supra Part IV.D.

435Nance, Over-Disciplining Students, supra note 1, at 1073.

436The SSOCS databases provide school security information only on a sample of public schools, see supra Part III, and much of that information, including the identity of the schools, is not publicly available.

437Cf. Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 361 (urging governments to require schools to report more detailed data with respect to their disciplinary practices).

438Cf. id. (urging federal and state lawmakers to incorporate disciplinary data into their accountability rubrics); Daniel J. Losen, Conclusion, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 241, 248 (Daniel J. Losen ed., 2015) (“Ultimately, until discipline data are incorporated into the broader accountability rubrics used by states to evaluate schools and districts, it is unlikely we will witness comprehensive and lasting improvements in the area of school discipline.”).

439Cf. Daniel J. Losen, Directions for Broad Policy Change, in Closing the School Discipline Gap: Equitable Remedies for Excessive Exclusion 15, 15 (Daniel J. Losen ed., 2015); Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 362 (urging lawmakers to make public all detailed information regarding school discipline to hold schools publicly accountable for their disciplinary decisions).

440Cf. Nance, Dismantling the School-to-Prison Pipeline, supra note 1, at 362.

441Cf. id.

442Cf. id.

443See Press Release, U.S. Dep’t of Educ., Persistent Disparities Found Through Comprehensive Civil Rights Survey Underscore Need for Continued Focus on Equity, King Says (June 7, 2016), http://www.ed.gov/news/press-releases/persistent-disparities-found-through-comprehensive-civil-rights-survey-underscore-need-continued-focus-equity-king-says (reporting that the total number of out-of-school suspensions decreased by 20% since the 2011–2012 school year). Importantly, however, the U.S. Department of Education also observed that the new data suggest that student discipline still “occurs in high numbers and disparities remain significant.” Id.