Activate Compliance: A Multilevel Study of Factors Associated With Activation of Body-Worn Cameras
Bibliographic record
Abstract
Body-worn cameras (BWCs) have quickly become popular tools in law enforcement. In theory, BWCs have the capacity to record all the time. However, due to privacy, legal, and practical concerns, cameras must be activated by officers in most jurisdictions. Early comments have raised concerns that officers would not activate their cameras in situations where there was a possibility that an intervention would not “look good” or when a situation might involve unnecessary or excessive use of force—posing a clear threat to transparency. The current study aims (1) to examine activation trends during a 10-month pilot to better understand officers’ compliance with departmental policies over time and (2) to identify situational and individual factors related to activation in situations where, based on a detailed policy, cameras should have been activated. Using generalized linear mixed models, camera activation was found to be significantly related to the nature of police–civilian encounters and officers’ personal characteristics such as experience and gender. Because suspicions of voluntary nonactivation in controversial situations can greatly affect police–citizen relations, this article concludes on a discussion of automatic activation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".