The Elusive Nature of Gender Equity in Canadian Graduate Engineering Programs
Bibliographic record
Abstract
The Elusive Nature of Gender Equity in Canadian Graduate Engineering Programs Juliette SweeneyDoctor of Philosophy Department of Leadership, Higher and Adult EducationOntario Institute for Studies in Education University of Toronto 2024 Abstract The “problem” of women in engineering, or rather what causes the lack of women in engineering, has been debated for decades within academia and industry; however, despite agreement that more women are needed in engineering, the proportion of Canadian licensed engineers who were women in 2023 was 20%. Canadian graduate engineering schools are a critical source of future engineering leaders but in 2022 only 27% of the graduating class was women. In this study I used a conceptual framework based on the work of Bourdieu and Suchman who argue that organizations seek various forms of legitimacy to justify their practices and policies. This study examines how Canadian graduate engineering schools reconcile the perceived tension between practices promoting gender equity and those enforcing academic rigor and how this tension reflects conflict between claims of moral and cognitive legitimacy. This research also analysed how tensions between equity and meritocracy inform the experiences of faculty and students. This work is important as gender wage and opportunity gaps in engineering need to be addressed. The engineering workforce also needs to become more heterogeneous as homogenous design teams produce flawed and inequitable products, such as air bags, facial recognition technology, and oximeters. The study included three phases: firstly, data calculations established women’s participation in graduate engineering programs over 2000-2019; secondly, equity, diversity and inclusion (EDI) and admission webpage analysis established how institutions attempted to reconcile the perceived tension between equity and meritocracy; and thirdly, interviews with 10 faculty and 20 students within a comparative case study at two institutions explored how these tensions played out within participants’ experiences. Findings included that: the proportion of women in graduate programs increased three percentage points between 2000-2019, institutions struggled to reconcile tensions between equity and meritocracy on their webpages, and participants seldom discussed diversity as including gender. Students and faculty experienced backlash to EDI and gender equity initiatives, men often ignored women’s contributions, and meritocratic practices promoted masculinist norms. Informal admission processes seldom considered equity and, at times, were neither equitable nor meritocratic. Participants saw senior leadership as critical in changing institutional culture and called for administration to acknowledge and address systemic barriers.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".