Less of a minority in university education in engineering? An intersectional analysis of female and male students in Canada
Bibliographic record
Abstract
While engineering remains a male dominated university program in the industrialized world, in Canada the proportion of women enrolled in undergraduate engineering education has risen in some universities. Furthermore, the percentage of undergraduate female students varies considerably among engineering sub-disciplines. Considering three selected Canadian universities, each with a relatively high proportion of women in undergraduate engineering programs, this interdisciplinary mixed methods study first explains the rationale for its methodology, namely, an intersectional gendered research design drawing on the perspectives of female and male students and faculty members in various engineering sub-disciplines, and of administrators in the engineering programs. We then provide a feminist intersectional overview of the students’ personal backgrounds and of student life. The article concludes by highlighting the contributions made by our feminist intersectional analysis toward gaining a deeper understanding of the complex relationship between gender and contextual variables such as university and type of program with various indicators of the background and experiences of students in selected Canadian undergraduate engineering programs. Questions for further research about women as a minority among university students are also identified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".