“Stop Resisting or You’re Gonna Get It Again”: Police Use of Force in a Canadian Crime Reality Television Show
Bibliographic record
Abstract
Extant research suggests that crime reality television advances biased narratives about offenders, police officers, and the nature of crime. This study contributes to the literature by investigating the prevalence, severity, and proportionality of police use of force in Under Arrest, a Canadian crime reality television show. Using a content analysis of all 65 Under Arrest episodes, research reveals that police use of physical force is portrayed in nearly half of vignettes, and over half of force used is excessive force. Logistic regression indicates that the racial composition of the suspect pool does not predict use of force when controlling for other relevant variables. However, suspect race is the strongest predictor of excessive force even when controlling for initial crime seriousness, gender, suspect intoxication, and weapon possession. Results indicate that Under Arrest contributes to law and order ideology by portraying police use of force as a necessary and justified tool for controlling crime committed by racialized suspects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".