Reflections on gender disparity in STEM higher education programs: Perspectives and strategies
Bibliographic record
Abstract
STEM has been broadly perceived as a vital driver of sustainable development worldwide. However, women remain underrepresented in STEM fields despite decades of effort to bridge the gender gap. The purpose of this article is to understand of the underlying factors that contribute to gender disparity in STEM and suggest effective interventions. Specifically, I seek to address the following questions: what factors contribute to gender disparity in graduate STEM program? What strategies can be adopted to address the issue?This paper examines fifty articles published from 2006-2016 that had women or gender and/or science as a central part of their studies to identify institutional and socio-cultural perspectives used to explain the situation and identify strategies that have been used to overcome the problem. Findings suggest that the factors that contribute to women underrepresentation in STEM are complex and numerous, calling for multi-faceted strategies to move the field forward.
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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 | 0.038 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.007 | 0.008 |
| 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".