Who has long commutes to low-wage jobs? Gender, race, and access to work in the New York region
Bibliographic record
Abstract
Geographies of home and work have changed as public investment has favored central and distant suburban locations and as income inequality has increased. These changes result in shifting geographies of advantage that (dis)benefit gender and racial/ethnic groups unevenly. We examine commuting differentials by gender and race/ethnicity based on combinations of wages and commute times using data for the New York region.We find that Black, Asian, and Hispanic women and men are concentrated in jobs that have long commutes and low-wages, and Black and Hispanic workers’ concentrations increased from 2000–2010.Although Asian men and women remain overrepresented in that category, their share decreased in the 2000's.The urban core has become a region of heightened advantage, as White men, and an increasing share of White women, commute short times to well-paid jobs. Disadvantage has expanded for Black and Latina women whose long commutes are not compensated by well-paid employment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".