Who has long commutes to low-wage jobs? Gender, race, and access to work in the New York region
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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