Motherhood in Academia : A Novel Dataset with an Application to Maternity Leave Uptake
Bibliographic record
Abstract
Legislation over the past two decades enhanced the availability and quantity of statutory maternity leave in the United Kingdom. In high-skilled sectors, many employers top up this maternity leave in an effort to retain and develop the careers of women. As leave provision became more generous, debates emerged as to the role, if any, these enhanced benefits have in retaining women in high status occupation and facilitating their career growth. Further, individual situations and employment status may prevent women from taking advantage of enhanced benefits. This paper presents findings from a comprehensive survey of thousands of women in the UK Higher Education sector and documents how the lives of academic mothers changed over the past quarter century. Contract status and the partner’s participation in parenting has significant effects on the types of maternity leave taken. We reflect on these findings and discuss future research in the area of labour market equity and productivity the availability of this comprehensive quantitative survey of academic women can facilitate.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".