Women on the move for science, technology, engineering and mathematics: Gender selectivity in higher education student migration
Bibliographic record
Abstract
Abstract Despite the gendered rebalancing of enrolments in higher education (HE) in the West, the underrepresentation of women in science, technology, engineering and mathematics (STEM) disciplines persists. Gendered selectivity of field of study influences higher education student migration (HESM) and in turn sheds light on HE participation. Framed by gender intersectionality theories both in HE studies and migration scholarship, this paper uses innovative data to analyse the intersectional effect of gender and field of study on HESM in Canada. Based on Statistics Canada's postsecondary student information system for the 2019/20 academic year, Canadian interregional flow matrixes structured by gender, field and level of study are constructed and analysed. The results show compelling evidence of the influence of gendered differences in HESM when intersected with field and level of study. Notably, women pursuing STEM studies migrate significantly more than any other grouping (i.e. gender, field and level of study groupings). The paper concludes with a discussion of policy implications for the influence of HESM on community demographic make‐up and local labour markets, as well as future research including the need to understand gendered dimensions of migration intentions and motivations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".