The Need for a Canadian Database of Police Use-of-Force Incidents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it