The Intersectional Experiences of Women of Colour in Undergraduate Engineering
Bibliographic record
Abstract
Our students’ lived experiences shape their learning journey, but existing literature gives little insight into the challenges that women of colour in engineering face in Canada. We used reflexive thematic analysis to understand recurring themes from focus group conversations with 26 women of colour at a medium-sized, research-intensive university. We show women of colour who are pursuing engineering have undergraduate experiences that are affected by identity, academics, family and peer relationships. The generic multiple worlds theoretical construct helped explain the tension between worlds. Students adapted to new academic challenges with imposter syndrome, leadership frustration and high standards. Oftentimes, support from immigrant parents translates into expectations, adding pressure, while finding peers who shared similar identities and offered genuine support proved to be a challenge. Sometimes, academic settings and peers prevented women of colour from feeling included in engineering. Institutions should create nurturing environments for women of colour and actively engaging parents.
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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".