Race and Selective Enforcement in Public Housing
Bibliographic record
Abstract
Drugs, crime, and public housing are closely linked in policy and politics, and their nexus has animated several intensive drug enforcement programs targeted at public housing residents. In New York City, police systematically conduct “vertical patrols” in public housing buildings, making tens of thousands of Terry stops each year. During these patrols, both uniformed and undercover officers systematically move through the buildings, temporarily detaining and questioning residents and visitors, often at a low threshold of suspicion, and usually alleging trespass to justify the stop. We use a case‐control design to identify the effects of living in one of New York City's 330 public housing developments on the probability of stop, frisk, and arrest from 2004–2011. We find that the incidence rate ratio for trespass stops and arrests is more than two times greater in public housing than in the immediate surrounding neighborhoods. We decompose these effects using first differences models and find that the difference in percent black and Hispanic populations in public housing compared to the surrounding area predicts the disparity in trespass enforcement and enforcement of other criminal law violations. The pattern of racially selective enforcement suggests the potential for systemic violations of the Fourteenth Amendment's prohibition on racial discrimination.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".