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.
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 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".