Off to Market: Neighborhood and Individual Employment Barriers for Women in 21St Century American Cities
Bibliographic record
Abstract
:This paper endeavors to create a better understanding of the barriers to employment faced by disadvantaged urban women in the post–welfare reform era. Using data from the Project on Devolution and Urban Change, a unique geographically linked, longitudinal, multicity set of survey data, logistic regression models weighs the relative importance of individual barriers to employment (e.g., poor health, childcare, family responsibilities) and contextual or neighborhood barriers to employment (e.g., poverty rate, joblessness rate) on labor market outcomes. Results reveal that several neighborhood characteristics are predictive of employment outcomes, including automobile access, female-headedness, vacancy, and disorder. Results suggest a more complex, nuanced interplay between neighborhood-level variables and individually measured variables in preventing some women from obtaining both modestly paying employment with few allocated hours of work per week, and also better-paying jobs with more hours of work per week.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".