Toward victim‐sensitive body‐worn camera policy: Initial insights
Bibliographic record
Abstract
Abstract Research Summary Despite constituting a substantial portion of police contacts, victims in general, and violence against women (VAW) survivors in particular, have received little attention in body‐worn camera (BWC) research. As BWCs proliferate in policing, crafting victim‐sensitive BWC policies is important. Drawing from qualitative interviews with 33 survivors of sexual assault and/or intimate partner violence, we identify themes that characterize victim‐sensitive BWC policies: notification, consent, alternative recording options, procedural consistency, and data storage and access. These findings lay a foundation for further research that can assess the generalizability of these themes to other samples of survivors. Policy Implications VAW survivors are stakeholders who should be consulted in the production of victim‐sensitive BWC policy for police services. This exploratory study suggests that BWC use will be more victim‐sensitive when (1) officers notify victims of BWC use as soon as reasonably possible during an interaction, (2) officers ask victims if they consent to BWC recording, (3) officers deactivate the video recording function of the BWC (or reposition the BWC's lens away from the victim) if consent is not provided or if doing so would make the victim more comfortable, (4) police services ensure that BWCs are used consistently by frontline members, that BWC videos are regularly subject to supervisory review, and that videos are appropriately used in training to prepare for quality survivor‐police interactions, and (5) officers and services provide victims with clear information regarding BWC footage access and data security.
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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 | 0.034 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".