Racial/Ethnic Connection with Confidence in the Police: Equal Treatment Matters
Bibliographic record
Abstract
Recent discussions on confidence in the police by race/ethnicity call for shifting the research focus from whether race/ethnicity matters to why and how it matters. The purpose of this article is to decipher the mediating role of the quality of police treatment in a nuanced study of racial impact on confidence in the police. Data were collected from a two-wave random-sample telephone survey of approximately 2400 residents in Houston, TX. The results confirm the expected effect of race/ethnicity on confidence in the police, net of neighborhood contexts and respondents’ demographics. More importantly, we found that the three measures tapping into the quality of police treatment during police–resident encounters partially mediate the race/ethnicity effect on views of police. Perceived equal treatment emerged as having the strongest effect. When the combined race/ethnicity sample was divided into three racial/ethnic subsamples, perceived equal treatment exerted the largest effects on confidence in the police both within and across the groups. Its effect is most pronounced for the Black subsample. Implications for future research and policy are discussed.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".