Bibliographic record
Abstract
This study is one of the few to investigate correlates of force in the Canadian context. It also investigates the existence of protective factors that decrease the level of force used by the police. A total of 1,174 self-reported uses of force are analyzed. Multinomial logistic regression models were used to identify factors related to three possibilities: The force used by the police was lower than, equal to, or higher than the level of subject resistance. The analysis reveals that the impact of individual characteristics on the correspondence between officer force and subject resistance is negligible. Also, three general patterns of relationships are found. First, the presence of a weapon helps distinguish lower-than-expected force situations. Second, the presence of a single officer, resistance toward officer(s), conflict between the subject and another citizen, and subject intoxication have linear effects, that is, the effect increases or decreases consistently. Third, for every less severe level of force that was used, cases are more likely to be in the expected than the lower-than- and in the higher-than-expected group. The findings obtained in this study are consistent with the literature, suggesting that it is reasonable to apply most conclusions from previous studies on police use of force to the Canadian context. The analysis also suggests that police use of force could be better understood as a trichotomy where the force used by the police is depicted as lower than, equal to, or higher than the level of subject resistance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".