“EXPERIENCE OF THE EXPECTED?” RACE AND ETHNICITY DIFFERENCES IN THE EFFECTS OF POLICE CONTACT ON YOUTH*
Bibliographic record
Abstract
Abstract Proponents of police reform have called for changes in the way police interact with citizens, particularly with people of color. The rationale, in part, is that when people have more favorable perceptions of their police encounters, they view the police as more just and are more willing to cooperate and comply with the law. To assess whether perceptions of police‐initiated encounters shape law‐related outcomes, we examine how satisfaction with treatment during prior police contact affects procedural injustice, reporting intentions, norms supporting the use of violence, and delinquency. We also explore whether these relationships vary among Blacks, Whites, and Latinos. Our results indicate that youth who have been stopped or arrested fare worse than their counterparts with no police‐initiated contact; however, the potentially negative ramifications of these encounters on all outcomes except violence norms are generally mitigated when youth are satisfied with their treatment. The effects of contact are mostly invariant across racial/ethnic groups when a robust set of control variables are included. We conclude that changing the perceptions of youth regarding how they are treated by the police may mitigate some of the harms of being stopped or arrested, but we caution that these perceptions are shaped by factors aside from police behavior during encounters.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".