To Swerve and Neglect: De-Policing Throughout Today’s Front-Line Police Work
Bibliographic record
Abstract
This mixed methods study investigates how American and Canadian front-line police officers are responding to policing's new visibility, which implicates citizen-generated mobile device and CCTV footage and concomitant online interconnectivity and social media discourse, and to intensified scrutiny of officers' actions by a more critical public audience. Quantitative and qualitative data was collected from 3,660 rank-and-file officers at 23 police agencies across Canada and throughout the State of New York.This study finds that a substantial majority of today's rank-and-file officers in the 23 jurisdictions across both countries (72%) are intentionally reducing, or eliminating, proactive interactions in the community, in response to officers' perceptions that such discretionary initiatives are unnecessarily risky. Little variation was found across location and agency variables (country, region, and police agency size) or across individual officers' demographic variables (gender and race/ethnicity). For individual officers, the decision to practice de-policing and any subsequent intensification in an officer's de-policing practices, is associated with the accumulation of negative police-citizen interactions over an officer's years of front-line police service. Influence from the rank-and-file police subculture also plays a significant role in contributing to these widespread risk-averse practices.For many officers, de-policing is connected with attitudes toward, and avoidance of, individuals perceived as presenting with mental health issues and/or a 'non-traditional' sexual orientation, and, even more strongly, in relation to persons perceived by front-line officers as visible racialized minorities. Implications of the methodology and findings are discussed, including those in relation to the author's role as a 'pracademic' researcher, to the current and future situation of policing in North America, and to the author's efforts to enter into public debates regarding today's policing.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".