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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".