Women's leadership gamut in Saudi Arabia's higher education sector
Bibliographic record
Abstract
Abstract This paper explores women's leadership in Saudi Arabia's three university settings—gender segregated (women or men‐only), unsegregated (co‐educational) and the majority of partially segregated universities where women's campuses exist within male‐dominated universities. While Saudi Arabia's accelerated reforms are creating new opportunities for women's leadership, these are not reflected in the higher education sector yet. In adopting a feminist institutional theory perspective, this study employed a feminist qualitative approach, including 14 semi‐structured interviews in Saudi Arabia's three university settings. The findings revealed that the barriers to women's leadership were most significant within the partially segregated universities, rendering women leaders as effectively powerless. In contrast, women's leadership flourished in the women‐only university setting. As such, the findings suggest that the dominating partially segregated model is ineffective and problematic for women's leadership, and contradict the dominant view that gender segregation disempowers women. These insights have implications for the transformation of Saudi Arabia's higher education sector, aligned with the Kingdom's Vision 2030 policy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".