Bibliographic record
Abstract
This brief is based on the following published work:Saulnier, A., & Abbatangelo, J. (2024). Body-worn camera policy in Canadian policing. Canadian Public Policy, 50(1), 20-37. https://doi.org/10.3138/cpp.2023-032 Based on a survey of all federal, provincial, municipal, and First Nations police services in Canada, 36 of 172 Canadian police services are using body-worn cameras (BWCs) as of 2022. Twenty-seven of these services shared their BWC policy with the researchers of the source article. Almost all BWC policies provided activation instructions, required subject notification of BWC use as soon as reasonably possible, did not allow BWC footage to substitute for other forms of evidence, and permitted users to view their BWC footage. However, some important topics were not consistently discussed in existing policies, including issues around camera buffering, victim-sensitive practices, and services publicly disclosing BWC footage in the public interest. Police services should work towards using a nationally standardized BWC policy to promote evidence-based practice, increase public confidence in police, reduce resource wastage in services acquiring BWCs, and decrease liability for services using a shared standard.
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 | 0.068 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.023 | 0.030 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".