Police Legitimacy in Ethnic–Racially and Economically Stratified Democracies
Bibliographic record
Abstract
The issue of police legitimacy has increasingly occupied the attention of criminologists in the new millennium. Yet the study of police legitimacy is not without some controversies. This article provides a critical examination of some of the key themes emerging from the scientific literature on police legitimacy, specifically confidence in the police and procedural justice. In doing so, it juxtaposes democratic policing theory to the study of police legitimacy. Among other things, it is posited that the issue of race/ethnicity remains understudied in criminological research in both Canada and the United States. This is particularly true with respect to differential treatment within the criminal justice system across race/ethnic groups. One of the hallmarks of democratic policing is its even-handedness and the fairness of law enforcement interventions. The root cause of ethnic–racial animosity must be explained if we want to understand police legitimacy fully in a society that has formally adopted a multicultural identity. It is concluded that police legitimacy should be understood within the tension between the tall order of democratic principles and the reality of social and ethnic–racial stratification.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".