Experiences of female early-career professionals in male-dominated STEM companies in Kazakhstan
Bibliographic record
Abstract
After the collapse of the Soviet Union, Kazakhstan put forward elaborate initiatives to address gender segregation in the labour market. However, female professionals are still heavily underrepresented in the fields of science, technology, engineering and mathematics. Considering conflicting cultural influences on the role of women in the country and guided by the social cognitive theory, the present study explores the early career experiences of female professionals working in STEM companies in Kazakhstan. The thematic analysis of 24 semi-structured interviews with early-career female professionals showed that social structural challenges with recruitment and promotion, organization integration and work-life balance prevent women from building successful careers in STEM. It was also found that to counterbalance these challenges, female professionals develop a variety of coping strategies such as projecting a professional image and adopting a masculine interaction style, conforming to the existing status quo and challenging gender discrimination and stigmatization. Drawing from the social cognitive theory, this study revealed that conflicting gender role expectations based on traditional, Western and Soviet cultural values might disempower and undermine women’s agency in transitioning to employment 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".