Policing the pandemic: estimating spatial and racialized inequities in New York City police enforcement of COVID-19 mandates
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.
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".