Women’s academic leadership in STEM: a systematic literature review on challenges, opportunities and strategies
Bibliographic record
Abstract
This study examines the intersection of gender equity and inclusion in education through a Systematic Literature Review of academic articles and conference proceedings focused on women's leadership in Science, Technology, Engineering, and Mathematics (STEM). The studies were collected in three languages. Portuguese, as the official language of the country where the study was developed; Spanish, because it covers countries in Latin America; and English, because it is the predominant language in global scientific literature. The review identified studies made it possible to define twenty categories of challenges, nine categories of opportunities associated with women's leadership in STEM, and sixteen categories of strategies to support their development within the field. Findings highlight the critical need for cultural and structural reforms, alongside the implementation of targeted policies within academic institutions, to foster equitable opportunities, enhance recognition, and promote women into leadership positions in higher education. These insights emphasize the importance of sustained efforts to advance gender equity and inclusivity in STEM academic leadership.
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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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".