Bibliographic record
Abstract
Although many people consider excessive police violence disconcerting, if, when, and how they voice their opinion or respond by taking some sort of action has generally remained empirically unknown. In the hope of understanding this process, Ross has developed a four-stage model, based on a review of the literature and on interviews with the relevant actors. He then uses this tool to analyze police violence that occurred in Toronto, Canada and New York City, over a fifteen-year period. To better focus the study, he uses in-depth case studies of three well-publicized cases of police violence from each city, matched on important criteria. This study addresses a difficult, timely, and important topic for victims, for police personnel, and for society. Ross concludes that, in general, most individuals do not respond to police use of excessive force; further, if and when they do usually depends on the context of the violence. Using both quantitative and qualitative methods, Ross's model integrates a variety of approaches to improve our understanding of how communities come to define and control the use of force by police, including literature on the role of media efforts and their impact upon police violence. The work concludes with an analysis of the reasons why people react so infrequently to incidents of excessive force.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".