Race and Selective Enforcement in Public Housing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 N ew Y ork C ity, 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 N ew Y ork C ity'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 F ourteenth A mendment's prohibition on racial discrimination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it