Factors Facilitating and Impeding Women’s Retention in Math-Intensive STEM Fields
Bibliographic record
Abstract
Increasingly, there has been a notable lack of female representation in math-intensive STEM fields, such as engineering, geosciences, mathematics, computer science, and physics. This issue persists despite the growing number of women graduating in STEM disciplines. Enhancing the number of women who complete their education and work in STEM sectors is a critical task. To address this issue effectively, it is crucial to understand the factors responsible for the underrepresentation of women, both among STEM students and within the workforce. This qualitative study explores the experiences of female undergraduate students enrolled in math-intensive STEM programs at universities to understand the factors contributing to the underrepresentation of women in STEM fields within the context of Kazakhstan. The analysis based on 29 interviews highlights a range of personal, distal, and proximal factors that may significantly influence the retention of women in math-intensive STEM fields. Furthermore, this article offers several recommendations to promote and support women’s involvement in STEM.
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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 | 0.003 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".