Racial Demographics Explain the Link Between Racial Disparities in Traffic Stops and County-Level Racial Attitudes
Bibliographic record
Abstract
Disparities in the treatment of Black and White Americans in police stops are pernicious and widespread. We examined racial disparities in police traffic stops by leveraging data on hundreds of U.S. counties from the Stanford Open Policing Project and corresponding county-level data on implicit and explicit racial attitudes from the Project Implicit research website. We found that Black-White traffic-stop disparities are associated with county-level implicit and explicit racial attitudes and that this association is attributable to racial demographics: Counties with a higher proportion of White residents had larger racial disparities in police traffic stops. We also examined racial disparities in several poststop outcomes (e.g., arrest rates) and found that they were not systematically related to racial attitudes, despite evidence of disparities. These findings indicate that racial disparities in counties' traffic stops are reliably linked to counties' racial attitudes and demographic compositions.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".