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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.008 |
| 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".