Police body-worn cameras and privacy: Views and concerns of officers and citizens
Bibliographic record
Abstract
Police body-worn cameras (BWC) have been lauded for their potential to increase transparency and accountability by documenting officers’ actions and interactions with citizens. However, despite their widespread use in recent years, several law enforcement agencies have been hesitant to adopt this technology because of privacy concerns. This article explores the views of police officers and citizens from the Canadian province of Quebec towards the use of BWCs. Specifically, it seeks to: (a) understand how officers feel about being monitored by BWCs and (b) assess citizens’ privacy concerns towards police BWCs. A mixed-method research design was used, including interviews and focus groups with 78 police officers, including 46 officers from four pilot sites, and a telephone survey of 1609 residents from the same sites. The results show that officers are concerned about the potential effects of BWCs on their privacy and the privacy of the public. One major area of concern is the impact it may have on their work performance and the use of adaptative measures that support them in carrying out challenging duties. By contrast, most citizens have no reservations about being recorded by a BWC. Certain individual characteristics—such as age and perceptions of the police—however, were associated with heightened privacy concerns. Without neglecting citizens’ privacy, this study provides insights into the development of BWC policies that preserve officers’ right to privacy and ability to fulfill their duty.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".