Garland v. VanDerStok: Brief of Major Cities Chiefs Association; Police Executive Research Forum; and Current and Former National Security Officials, Law Enforcement Officers, and Prosecutors as Amici Curiae in Support of Petitioners
Bibliographic record
Abstract
Amici are the Major Cities Chiefs Association, the Police Executive Research Forum, and numerous current and former national security officials, law enforcement officers, and prosecutors. The Major Cities Chiefs Association (MCCA) is a professional organization of police executives representing the largest cities in the United States and Canada. The mission of MCCA is to provide a forum for police executives from large population centers to address the challenges and issues of policing, to influence national and international policy that affects police services, to enhance the development of current and future police leaders, and to encourage and sponsor research that advances this mission. The Police Executive Research Forum (PERF) is a police research and policy organization which helps to improve the delivery of police services through the exercise of strong national leadership; public debate of political and criminal justice issues; and research and policy development. Since its founding in 1976, PERF has identified best practices on fundamental issues such as reducing police use of force; developing community policing and problem-oriented policing; using technologies to deliver police services to the community; and evaluating crime reduction strategies. Amici also include former federal national security officials and current and former state and federal law enforcement officials and prosecutors who have significant experience protecting our communities from gun violence by international terrorists, drug cartels, domestic violent extremists, and other malign actors. Amici represent a broad cross section of the national security and law enforcement community, including former career and politically appointed federal officials across Republican and Democratic administrations, from the National Security Council staff, Office of the Director of National Intelligence, Central Intelligence Agency, Department of Homeland Security, Department of Justice, Federal Bureau of Investigation, Department of Defense, and Department of State, as well as current and former state law enforcement officers and prosecutors. A complete list of amici is included in the Appendix. As experienced counterterrorism officials, prosecutors, law enforcement officers, and professional organizations representing police, amici understand that unfettered access to firearms poses a grave national security and public safety threat to the nation. Regulations requiring pre-sale background checks, serial numbers, and record-keeping are crucial to preventing malign actors from readily obtaining the lethal firearms they use to commit acts of terror and other violent crimes. Dangerous weapons easily and inexpensively assembled from kits — known as “ghost guns” because they have no serial numbers — have proliferated throughout the country, where malign actors actively seek to acquire them. Ghost guns severely hamper law enforcement’s effort to combat violent crime, terrorism, domestic extremism, and firearms trafficking, to the detriment of both public safety and national security. Amici have a strong interest in the regulation of ghost guns to prevent them from falling into the hands of those who aim to harm the United States and its citizens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.036 | 0.027 |
| Insufficient payload (model declined to judge) | 0.026 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".