Approaching or avoiding? Gender asymmetry in reactions to prior job search outcomes by gig workers in female- versus male-typed job domains
Bibliographic record
Abstract
Abstract Despite recent increases in females entering male-typed job domains, women are more likely to exit these jobs than men, leading to a “leaky-pipeline” phenomenon and contributing to continued occupational gender segregation. Extant work has demonstrated that women are less likely to reapply to employers who previously rejected them for jobs in male-typed job domains. However, these studies leave unexamined whether women will reapply to other employers in those job domains and, if so, whether this pattern differs in female-typed job domains, hampering our confidence in the contribution of these patterns to gender segregation. This paper investigates whether employer rejection dampens women’s job-seeking persistence more than men’s for all employers and across male versus female job domains. Regression analyses of more than 700,000 applications for over 200,000 job postings by roughly 70,000 freelancers in an online contract labor market demonstrate that women are more likely than men to reduce job-seeking activity from all employers following rejections in the male-typed IT and programming job domain. Women are also more likely than men to seek jobs in other domains outside IT and programming following job-seeking rejection. By contrast, female freelancers in female-typed writing and translation jobs do not exhibit similar gendered behavior patterns. Implications for research on gender segregation, careers, and hiring are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".