Does Selective Crime Reporting Influence Our Ability to Detect Racial Discrimination in the Nypd’s Stop-and-Frisk Program?
Bibliographic record
Abstract
Abstract Prior analyses of racial bias in the New York City’s Stop-and-Frisk program implicitly assumed that potential bias of police officers did not vary by crime type and that their decision of which type of crime to report as the basis for the stop did not exhibit any bias. In this paper, we first extend the hit rates model to consider crime type heterogeneity in racial bias and police officer decisions of reported crime type. Second, we reevaluate the program while accounting for heterogeneity in bias along crime types and for the sample selection which may arise from conditioning on crime type. We present evidence that differences in biases across crime types are substantial and specification tests support incorporating corrections for selective crime reporting. However, the main findings on racial bias do not differ sharply once accounting for this choice-based selection.
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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 | 0.002 | 0.001 |
| 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".