Criminology Explains Police Violence, by Philip Matthew Stinson, Sr
Bibliographic record
Abstract
But ‘who polices the police’ (p. 1)? The extent of police violence is difficult to assess. Despite growing efforts to study police violence, or police misconduct more generally, research initiatives often face a particular problem: the severe lack of available data. Philip M. Stinson, the author of the book Criminology Explains Police Violence and professor of criminal justice at Bowling Green State University, believes that the issue of violence perpetrated by police officers is still largely overlooked in the USA. While many experts conclude that police crime remains an isolated phenomenon perpetrated by only a few ‘bad apples’, Stinson suggests that there is something more complex about police work that leads some officers to commit acts of misconduct. Whether it is the sense of entitlement that comes with a badge and a gun or the significant lack of accountability officers face, the causes of police violence, he argues, need to be further explored.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".