The Spatial Risk of Assault on Police Officers in Toronto, Ontario
Bibliographic record
Abstract
Since September 12th, 2022, nine police officers in Canada have been fatally assaulted in the line of duty. These officer deaths raise important questions concerning the nature of risks police face on duty, as well as the ways we can better understand those risks. Utilizing a Risk Terrain Modelling (RTM) approach, this study examined the risk of assault to police officers in Toronto, Ontario using Assault to Peace Officer data from January 1st, 2022, to December 31st, 2022. This study revealed that the risk of assault to police varies by the physical features present in each Division of the Toronto Police Service. Specifically, 7 features of the physical environment were found to increase risk of assault to police, with locations with high previous rates of robbery posing the most consistent risk. The resulting output provides The Toronto Police Service and policymakers with information and suggestions on how to utilize RTM methods to improve the safety of officers on-duty.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 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 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".