Bibliographic record
Abstract
We would like to thank seminar participants at the University of Chicago and IUPUI for comments and discussions and Meejung Chin, Vanessa Coca, and especially Anirban Basu, for excellent research assistance. Computer support was graciously provided by the Population Center at the University of Chicago. Please direct any comments to t-deleire@uchicago.edu and hlevy@uchicago.edu. Gender, Occupation Choice and the Risk of Death at Work Abstract: Women and men tend to work in different occupations. There has been substantial movement over the last forty years toward a more even distribution of men and women across occupations, but differences persist. Although a great deal of research has been devoted to the measurement of trends in occupation segregation by gender, very little work has focused on the underlying job choice process that generates this segregation. What makes men and women choose the jobs they do? Using employment data from the 1995- 1998 Current Population Surveys and data on occupational injuries and deaths from the Bureau of Labor Statistics, we estimate conditional logit models of occupation choice as a function of the risk of work-related death and other job characteristics. Our results suggest that women choose safer jobs than men. Within gender, we find that single moms or dads are most averse to fatal risk, presumably because they have the most to lose. The effect of parenthood on married women is larger than its effect on married men, which is consistent with the idea that men’s contributions to raising children are more fully insured than women’s. Overall, men and women’s different preferences for risk can explain about one-quarter of the fact that men and women choose different occupations
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".