Bibliographic record
Abstract
This paper will explore the dichotomy between the privacy concerns associated with the use of Body-Worn Cameras (“BWCs”) by law enforcement agencies, and the benefits associated with this technology, such as the evidential value of the BWCs video, audio, and images as reliable forms of evidence assisting courts and criminal justice players in making substantiated decisions and reaching just verdicts. The paper will provide a background overview of BWCs and the approach to their use in some Canadian jurisdictions, followed by a discussion on Canada’s struggles guarding the privacy of Canadians and the recent breaches of privacy conducted by the Royal Canadian Mounted Police (“RCMP”). Next, there will be a case-study section exemplifying the numerous flexible features and benefits of BWCs and produced digital evidence used in courts and police operations, followed by a section addressing the rule of law and the need for punishing police misconduct for mishandling highly sensitive information (such as that captured by BWCs). Lastly, the paper will reflect on its findings, discuss existing tensions, and propose a path forward for the safe and broad implementation of BWCs across Canada.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 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".