Knowledge Mobilization with Black and Racialized Communities on the Topic of Use of Force in the Greater Toronto Hamilton Area
Bibliographic record
Abstract
This presentation shows racial disparities in use-of-force data from police services in the Greater Toronto and Hamilton Area. Black, Indigenous, and racialized communities have long identified these disparities in Canada. While their concerns were often dismissed, the Ontario government mandated in 2020 that police collect race-based use-of-force data under the Anti-Racism Act, 2018. After five years of data collection, the issue of racial disparities in use-of-force persists. Specifically, Black, Indigenous, and racialized communities continue to face a disproportionate amount of extreme use of force compared to White communities. Analysis of use-of-force data from 2023 and 2024 shows that Racialized youth, Indigenous men and women, uniquely suffer from extreme use of force at higher rates. This pattern highlights a severe and targeted impact on racialized communities. Taken together, these findings underscore the urgent need for the provincial government and police services to develop and publicly commit to concrete policy and transparent accountability measures to address ongoing disparities in their interactions with Black, Indigenous, and Racialized communities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".