Tracking (In)justice: Uncovering Potential Biases in Canadian Incidents of Police Use of Force Resulting in Fatality
Bibliographic record
Abstract
The current body of literature on lethal use of force indicates a tendency for greater force utilization against males than females, and an overrepresentation of racialized individuals in use of force statistics. However, little research on the factors surrounding lethal force has been done within a Canadian context. To address this gap, the present study utilized the Tracking (In)Justice dataset, which tracks cases of use of force resulting in fatality in Canada, to explore victim demographics, types of force employed, incident locations, and changes in victim race over time. Analysis revealed that males constituted the majority of victims, and Indigenous and Black individuals were disproportionately victimized. The highest level of force used was consistent across genders and races, with firearms as the primary method of force. Additionally, certain provinces had higher incidences of Indigenous and Black victims, and victim demographics were found to exhibit annual fluctuations. By providing a better understanding of who has been victimized by lethal use of force in Canada, as well as where and when, the current study acts as groundwork for future studies to delve further into the contextual factors surrounding these incidents. However, the current study was limited by missing data, limited variables collected, and the narrow scope of incidents, which do not reflect the true prevalence of lethal use of force decisions in Canada. Ultimately, the results and limitations of the study underscore the necessity for a standardized national database of lethal use of force to better comprehend the phenomenon within 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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".