Canadian Policing and Body-Worn Cameras: Factors to Contemplate in Developing Body-Worn Camera Policy
Bibliographic record
Abstract
Body-worn cameras (BWCs) are increasingly being used by police worldwide. This study demonstrates that, as of 2019, at least 36 percent of Canadian police services have considered or trialed BWCs. News reports suggest that this number continued to rise in 2020. In this article and the accompanying appendices, we strive to provide a comprehensive summary of all topics that Canadian police services should address in a BWC policy. These topics fall into six general categories: BWC program, users, supervisors, data management and retention, video disclosure, and other expectations. The summary was produced by situating the contents of existing Canadian BWC policies in relation to key international content (e.g., BWC research and policy guidelines) and Canadian content (e.g., domestic BWC research, policy recommendations, and legislation) relevant to BWC policy. The summary we present is not prescriptive on topics that require further evidence or that would be best established by practitioners working in conjunction with key stakeholders (e.g., Canadian privacy organizations). We advocate for standardizing police BWC policy across Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.030 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".