Women’s Science, Technology, Engineering, and Mathematics Persistence After University Graduation: Insights From Kazakhstan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Women’s persistence in science, technology, engineering, and mathematics (STEM) has been widely researched in educational settings, whereas less is known about their STEM persistence after graduation. Drawing on social cognitive career theory and in-depth semi-structured interviews with twenty women graduates majoring in STEM fields, this article explores women’s persistence in STEM fields in Kazakhstan within four years after university graduation. The findings of the study are mapped around four themes—STEM self-efficacy beliefs, STEM career outcome expectations, organizational factors, and socio-structural factors—that are found important in shaping STEM women’s post-graduation career choices. The study also reveals factors accounting for disparities in women’s STEM persistence across different STEM fields. Implications highlight the need for more work at organizational and socio-structural levels to develop favorable conditions motivating and enabling women to persist in STEM careers within a patriarchal context.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it