To STEM or not to STEM: A cross-national analysis of gender and tertiary graduates in science, technology, engineering, and math, 1998–2018
Bibliographic record
Abstract
The comparative literature on gender and higher education has increasingly focused on differences in access to the fields of science, technology, engineering, and math (STEM). We contribute to this literature through a cross-national analysis of STEM graduates by gender between 1998 and 2018 across 90 countries. Many earlier studies emphasize the positive influence of a global liberal culture on women. More recent scholarship contends that women may be steered away from attaining a STEM degree in more liberal and individualistic societies. Our study shows a lower percentage of women graduates in STEM in countries that are more liberal. However, we find that the opposite is the case for men. Our findings are consistent with the idea that individuals in more liberal cultural contexts are more likely to make degree decisions based on individual preferences that are influenced by gendered societal norms. Both women and men are more likely to “indulge in their gendered selves” in these cultural contexts. Our findings are inconsistent with the idea that liberal modernity influences men and women in STEM in a gender-neutral mode.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".