Graduate Engineering Programs: Admission, Success, Support and Institutional Culture
Bibliographic record
Abstract
This study examined the experience of students within Canadian graduate engineering programs and explored how institutional culture impacted access to these programs and student support resources.The paper discusses how admission processes, institutional culture and support resources impact women within graduate engineering programs and presents recommendations to potentially widen student access and support student success.As part of a mixed methods study, 10 faculty and 20 students were interviewed at two large Canadian graduate engineering schools to gather data on participants' perceptions of admission processes, institutional culture and support resources.Equal numbers of women and men were interviewed to determine if gendered experiences or perceptions of the culture were experienced and all interview data was anonymized to protect the identity of participants.Findings indicate that informal practices, containing unspoken rules, ran parallel to formal admission processes; certain experiences of institutional culture were gendered, particularly around notions of success; and support systems were often lacking or insensitive to the needs of graduate students.Participants offered a number of suggestions to improve support systems.This paper increases our knowledge regarding Canadian graduate engineering schools by identifying realities parallel to formal admission practices, describing institutional culture, and analyzing graduate students' perception of support systems.It concludes that informal admission practices should be acknowledged to widen access, that institutional cultural change regarding DEI is problematic and support resources could be improved to better serve all graduate students, particularly women and other under-represented groups.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".