Academic women’s careers and the motherhood penalty: intersectional challenges in the Arab Middle East
Bibliographic record
Abstract
Purpose This study, informed by intersectionality and social role theories, aims to explore the interplay among micro-level factors (e.g. gender and professional identities, pregnancy and maternity) and macro-level elements (e.g. institutional policies and employment contracts) and their implications for academic career trajectories for women in an Arab Middle Eastern context. Design/methodology/approach Data were collected through comprehensive, face-to-face interviews with 20 women affiliated with four universities in Jordan. These participants included both current mothers and those expecting to become mothers. Findings Our findings highlight significant academic disadvantages, where women in academia face compounded challenges arising from the intersection of their motherhood status and the demands of maintaining a professional identity. Unclear maternity policies and limited institutional support during pregnancy lead to structural disadvantages, exacerbating the work-family conflict for women in academia. Practical implications From a policy perspective, our findings inform university policymakers on supporting women academics during pregnancy and motherhood, enhancing inclusion and diversity and reducing work-family conflict and gender discriminatory stereotypes. Originality/value This study offers a theoretical contribution by extending intersectionality and social role theories into the context of academic career development, highlighting how institutional structures and identity-based norms shape women’s career trajectories in the Arab Middle East. Practically, it provides valuable insights for organizational policy, particularly in areas such as maternity support, employment practices and gender-inclusive career progression frameworks in higher education.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".