Visitor Inflows and Police Use of Force in a Canadian City
Bibliographic record
Abstract
Early ethnographic studies suggested that police intervention should be understood within its larger context. Still, the number of studies focused on the ecology of force remains small compared to those of studies on individual and situational factors. Furthermore, Canada remains nearly absent from the academic literature on police use of force. Assuming that force does not occur in a spatially random manner, this article aims to test propositions for the main macrosociological perspectives in the use-of-force literature: social disorganization theory, the minority-threat hypothesis, and the theory of police rigour. Another purpose of this study is to investigate whether, at the level of the census tract (CT), visitor inflows are predictive of police action. Negative binomial regression modelling is used to predict the occurrence of 1,411 self-reported uses of force in 506 CTs. The findings show that social disorganization is the most predictive explanation for the frequency of use-of-force situations in an area. The analysis also supports the proposition that the frequency of use-of-force situations is positively related to the level of crime in the area. While the inclusion of visitor inflows significantly improves the analysis of spatial variations of police use of force, it contributes relatively little relative to other explanations. No support was found for the minority-threat hypothesis, nor for Klinger's theory of police vigour.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".