Determinants of Citizens’ Perceptions of Police Behavior During Traffic and Pedestrian Stops
Bibliographic record
Abstract
A large body of research has examined public perceptions of police behavior. Many of these studies have raised concerns about perceptions of unequal treatment of citizens by law enforcement and the effects such disparate treatment might have on police–community relations. This scholarship has largely examined global perceptions of police behavior rather than asking about actual encounters with officers. Relying on global opinions of police, however, tends to distort perceptions as it tends to illicit prejudiced and stereotypical views about law enforcement rather than lived experiences. This article offers a more precise approach to measuring police behavior during encounters with citizens by assessing views of those who have had recent contact with law enforcement. Specifically, we examine how perceptions of police behavior during both traffic stops and street stops of pedestrians might vary according to a citizen’s sociodemographic background and geographic location and how such factors might influence perceptions of the legitimacy of their encounter with the officer. Results from our multivariate analyses suggest that youth, African Americans, the poor, and those living in large urban areas are significantly more likely than others to believe they were treated outside of the scope of acceptable police conduct. Furthermore, ethnic minorities, the poor, and those in urban areas are much more likely to perceive the stop as illegitimate. Our results suggest that much of this might be explained by differences in police behavior according to the size of the place and across different social groups.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".