Disrupting the status quo: fostering a practice of inclusion in engineering through post-secondary allyship training
Bibliographic record
Abstract
The traditionally male-dominated engineering profession requires greater inclusion and proportional representation of diverse groups to effectively solve complex global challenges. To foster inclusive practices, individuals involved in engineering education, including students, staff, and faculty, can be engaged, trained, and empowered to serve as “active allies”. In this study, researchers designed and trialed a blended training program meant to support potential allies to adopt a practice of inclusion. The 26 participants in our study included undergraduate and graduate students, staff, and faculty from a Canadian engineering college. Our study incorporated a transformative mixed methods design meant to qualify and quantify the impacts of the allyship course. Our findings show learners’ motivation and allyship competencies progressed because of the psychologically safe learning environment and course content. Additionally, participants experienced the largest improvements in understanding equity, diversity, and inclusion (EDI) language, which resulted in more frequent EDI conversations. However, conducting EDI training without an EDI organizational commitment poses a risk to sustained allyship behaviors. Our findings show that with organizational support, allyship training will promote inclusive behaviors necessary to create innovative, equitable, and diverse organizations—a necessary foundation for solving complex global problems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".