THE ROLE OF SUB-DISCIPLINE CHOICE IN WOMEN’S ENROLLMENT AND SUCCESS WITHIN CANADIAN UNDERGRADUATE ENGINEERING PROGRAMS.
Bibliographic record
Abstract
In Canada, the proportion of female students in engineering is considerably lower than the proportion of female students in higher education. Using Tinto’s (1993) theories concerning social and academic integration, this study investigated the relationships between the proportion of female undergraduate engineering students, and the proportion of female faculty, and departmental lead faculty. Using descriptive statistics, the study established that distinct and persistent differences exist in the proportions of female enrolment among schools and among sub-disciplines. This paper addresses a gap in the literature concerning the impact of sub-discipline choice on women's engagement and success within undergraduate engineering programs in Canada. The proportion of female students was found to vary considerably across the sub-disciplines, from 48% in biosystems to 15% in software engineering [14]. The paper will present female sub-discipline enrolment trends over time and discuss the impact of sub-discipline choice and institutional factors on female students' successful academic and social integration within Canadian engineering schools.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".