Policing the pandemic: estimating spatial and racialized inequities in New York City police enforcement of COVID-19 mandates
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
The use of policing to enforce public health guidelines has historically produced harmful consequences, and early evidence from the police enforcement of COVID-19 mandates suggested Black New Yorkers were disproportionately represented in arrests. The over-policing of Black and low-income neighborhoods during a pandemic risks increased transmission, potentially exacerbating existing health inequities. To assess racialized and class-based inequities in the enforcement of COVID-19 mandates at the ZIP-code-level, we conducted a retrospective spatial analysis of demographic factors and public health policing in New York City from March 12-May 24, 2020. Policing outcomes (COVID-19 criminal court summonses and public health and nuisance arrests) were measured using publicly available police administrative data. After controlling for two measures of social distancing compliance, a standard deviation increase in percentage of Black residents was associated with a 73% increase (95% CI: 35%, 123%) in the COVID-19-specific summons rate and a 34% increase (95% CI: 17%, 53%) in the public health and nuisance arrest rate. Percentage of Black residents and historical stop-and-frisk rates had stronger associations with COVID-19 summons rates than multiple measures of social distancing compliance. Findings demonstrate pronounced spatial and racialized inequities in pandemic policing of public health that mimic historical policing practices deemed racially discriminatory. If the field of public health supports criminalization and punishment as public health strategies, it risks reinscribing racialized health inequities.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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 it