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.
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.
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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".