Correlates of subject(ive) resistance in police use-of-force situations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose In most jurisdictions, resistance is the primary legal justification for police use of force. Identifying the correlates of resistance helps to anticipate non-compliance, increase officer safety, and maintain low rates of use of force. Following previous research on subject demeanor, the purpose of this paper is to argue that the presence of resistance is determined subjectively, based on an individual’s interpretation of a situation. Design/methodology/approach Binary and multinomial logistic regression models were used to analyze resistance reported in 878 interventions involving police use of force in a large Canadian city. A four-category measure similar to those commonly found in previous studies was used to build dependent variables and a series of 14 behaviors based on the actions of a subject was used as a predictor of reported resistance. Findings As expected, subject behavior was found to be a significant predictor of reported resistance. Officer and citizen characteristics (gender, race, age/experience) were weakly related to the outcome. Models were found to offer considerably better predictions when situational factors were included. Originality/value Perceptions of resistance were found to be influenced by a variety of factors, including, but not limited to, the subject’s actions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it