Getting ahead in the social sciences: How parenthood and publishing contribute to gender gaps in academic career advancement
Bibliographic record
Abstract
How do parenthood and publishing contribute to gender gaps in academic career advancement? While extensive research examines the causes of gender disparities in science, technology, engineering, and mathematics (STEM) careers, we know much less about the factors that constrain women's advancement in the social sciences. Combining detailed career- and administrative register data on 976 Danish social scientists in Business and Management, Economics, Political Science, Psychology, and Sociology (5703 person-years) that obtained a PhD degree between 2000 and 2015, we estimate gender differences in attainment of senior research positions and parse out how publication outputs, parenthood and parental leave contribute to these differences. Our approach is advantageous over previous longitudinal studies in that we track the careers and publication outputs of graduates from the outset of their PhD education and match this data with time-sensitive information on each individual's publication activities and family situation. In discrete time-event history models, we observe a ∼24 per cent female disadvantage in advancement likelihoods within the first 7 years after PhD graduation, with gender differences increasing over the observation period. A decomposition indicates that variations in publishing, parenthood and parental leave account for ∼ 40 per cent of the gender gap in career advancement, suggesting that other factors, including recruitment disparities, asymmetries in social capital and experiences of unequal treatment at work, may also constrain women's careers.
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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 | 0.009 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".