Geographies of Frontline Workers: Gender, Race, and Commuting in New York City
Bibliographic record
Abstract
The COVID-19 pandemic amplified social, economic, and environmental inequalities in American cities, including inequities in commuting and access to employment. Frontline workers—those who had to work on site during the pandemic—experienced these inequalities in every aspect of their daily lives. We examine the labor force characteristics and commuting of frontline workers in New York City with a focus on gender and race/ethnic disparities in wages and commuting modes and times. Using Census PUMS microdata for a sample of New York City residents in the 2015–2019 period, we identify frontline workers from detailed industry and occupation codes and compare characteristics of frontline workers with those of essential workers who could work remotely. The data highlight wide disparities between frontline and remote workers. Minority men and women are concentrated in the frontline workforce. The residential geographies of frontline and remote workers differ greatly, with the former concentrated in low- and moderate- income areas distant from work sites and with long commute times. Compared to men, women frontline workers rely heavily on public transit to commute and transit dependence is highest among Black and Latina women. Low-wage employment, long commute times, and transit dependence intersected to increase minority women’s economic and social vulnerability during the pandemic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".