Women leadership in higher education: exploring enablers and challenges from middle-level academics’ perspective
Bibliographic record
Abstract
The scarcity of literature in higher education addressing the enablers or challenging factors influencing the progression of women academics from middle-level to senior leadership roles is noteworthy. This study aims to explore the factors that enable or impede the advancement of middle-level women academics and identify recommendations for promoting women leadership in higher education institutions. This qualitative study has collected interview data from 17 informants from five Malaysian public universities. The thematic analysis revealed four enablers and three challenges faced by women in academic leadership. The thematic analysis revealed four enabler factors - gender-neutral policies, management of multiple roles, core personal competencies, and the presence of mentors and role models. Conversely, traditional women’s roles, social stigma, and personal factors were identified as factors that hinder women’s leadership in universities. The findings underscore the importance of implementing gender-neutral policies to foster inclusivity, the establishment of formal mentorship programmes, and cultivating multitasking skills among women. Understanding the dynamics from the perspective of middle-level women academics is essential for crafting a more equitable and supportive institutional environment to equip them with the necessary resources and support to transition into senior leadership positions smoothly, ultimately strengthening the leadership pipeline within the institution.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
| grok | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| opus | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".