Body worn cameras in policing: The value and costs of implementation in Canada
Bibliographic record
Abstract
The use of body worn cameras in policing has received increased interest following a number of high profile incidents in the USA since 2014. Body worn cameras have been touted as a tool to increase transparency and hold police more accountable during interactions with vulnerable sectors of the population. Political pressure and public opinion have encouraged police agencies in Canada to explore the use of these devices, despite the fact that they operate in a political and social climate that differs from that of the United States. An analysis of the effects of body worn cameras on police and public interactions, the capabilities of current body worn camera devices, concerns related to video surveillance by the police and the subsequent effect on privacy rights of individuals, and financial costs show many challenges and questionable benefit from the widespread use of body worn cameras by police particularly in the Canadian context.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".