The Impact of Police Deployment on Racial Disparities in Discretionary Searches
Bibliographic record
Abstract
A large and growing body of research finds racial disparities in discretionary searches of drivers during traffic stops with Black drivers disproportionately involved in these investigations. Among the explanations for these disparities is the deployment hypothesis which suggests that as police departments increasingly adopt hot spots policing strategies, proactive traffic stops and discretionary searches may spatially cluster around crime hot spots contributing to racial disparities. The present study builds on the existing research literature by identifying hot spots using reported crime data from a police department and examining whether these crime hot spots function as a mediating factor to the relationship between driver race and discretionary searches. Findings provide partial support for the deployment hypothesis. While nearly half of all traffic stops transpired within one quarter mile of hot spots and more frequently involved Black drivers, stops involving Black drivers remained more likely to include discretionary searches and increased concomitantly with distance from the nearest hot spot.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".