The Social Ecology of COVID-19 Cases and Deaths in New York City: The Role of Walkability, Wealth, and Race
Bibliographic record
Abstract
The present research examined the zip code level (177 zip codes) prevalence of and deaths associated with COVID-19 in New York City as of May 22, 2020. Walkable zip codes had consistently lower prevalence of ( r = −.49) and deaths ( r = −.15) associated with COVID-19. The mediation analysis showed that the degree of reduction in actual geographical mobility during the lockdown (measured by smartphone GIS data) accounted for geographical variations in the number of confirmed cases and deaths. Residents in wealthy zip codes and walkable zip codes were able to limit geographical mobility, whereas residents in poor zip codes and Black and Hispanic dominant zip codes were not. Finally, the spatial lag regression analysis showed that walkability was a robust predictor of zip code–level prevalence of and deaths associated with COVID-19. Overall, walkability seems to have provided protection against the spread of COVID-19.
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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 | 0.004 | 0.017 |
| 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 | 0.006 |
| 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".