Gender Equality in U.S. Labor Markets in the “Great Recession” of 2007–10
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
The -Great Recession of 2007-2009, the worst economic downturn faced by the U.S. economy since the Great Depression, has also come to be known as the -Great Man-cession in that job loss hit males harder than females. By contrast, this paper argues that the -man-cession story is far too simple. Using a broad range of indicators from the Current Population Survey (CPS) and taking a historical perspective, we show that several demographic groups have been especially hard hit by the recession, including African American males and females, Hispanic males and females, young females, and families maintained by single women. In addition, the gender gap in unemployment is much smaller once underemployed and marginally attached workers are counted. Data from the Current Employment Statistics cast further doubt on the man-cession story, indicating that women lost over 10 times more jobs in the current recession than in the previous two recessions compared to men, who lost 2.3 times more jobs. Following this review of the trends, the paper surveys federal and state government responses to the needs of workers hardest hit by the recession and concludes that -man-cession label has led to misidentification of the most vulnerable groups who should be the explicit beneficiaries of economic recovery policies.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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