Hiring and Intra-occupational Gender Segregation in Software Engineering
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
Women tend to be segregated into different subspecialties than men within male-dominated occupations, but the mechanisms contributing to such intra-occupational gender segregation remain obscure. In this study, I use data from an online recruiting platform and a survey to examine the hiring mechanisms leading to gender segregation within software engineering and development. I find that women are much more prevalent among workers hired in software quality assurance than in other software subspecialties. Importantly, jobs in software quality assurance are lower-paying and perceived as lower status than jobs in other software subspecialties. In examining the origins of this pattern, I find that it stems largely from women being more likely than men to apply for jobs in software quality assurance. Further, such gender differences in job applications are attenuated among candidates with stronger educational credentials, consistent with the idea that relevant accomplishments help mitigate gender differences in self-assessments of competence and belonging in these fields. Demand-side selection processes further contribute to gender segregation, as employers penalize candidates with quality assurance backgrounds, a subspecialty where women are overrepresented, when they apply for jobs in other, higher-status software subspecialties.
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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 | 0.000 | 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 it