The Need for a Canadian Database of Police Use-of-Force Incidents
Bibliographic record
Abstract
Concerns surrounding the use of force by police officers appear to be growing, fuelled by perceptions that the police use force too frequently, research showing that force is applied disproportionately to members of certain groups, and the view held by some that the mechanisms for holding police responsible for unjustified force are inadequate. In this paper, we advocate for the creation of a national use-of-force database in Canada to gain a better understanding of these issues, adding our voice to those who have already been actively calling for this. We describe some of the potential benefits that would be associated with such a database, including the fact that it would enhance police transparency and accountability, while also increasing our understanding of when and why force is used and what strategies may be useful for reducing inappropriate applications of force. We also highlight some of the challenges we think would be encountered, including mandating nationwide participation, overcoming resistance from the police community, establishing sensible case inclusion criteria, and standardizing data collection. While these are significant challenges, we believe not only that they are possible to overcome but that doing so will provide real value to Canadian society.
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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 | 0.019 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.020 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".