Engineering Inclusivity by Design: Co-Designing an Inclusive Innovation Workshop
Bibliographic record
Abstract
In North American universities, engineering faculties often exhibit unequal representation, with inclusivity in the curriculum hindered by aspects like elitism and technical social dualism. This project aimed to foster a more inclusive engineering culture by co-designing a workshop with students at McMaster University to appreciate diverse identities and incorporate equity principles into their work. The "Inclusive Innovation Design Challenge" workshop, attended by 55 students, introduced concepts of self-identity and positionality, followed by a human-centred design sprint based on equity-based co-design principles. Participants developed personas and brainstormed solutions to design challenges, enhancing awareness of the value of diverse perspectives. The outcome was overwhelmingly positive; feedback from a post-workshop survey indicated a shift in participants' perceptions towards their identities and the inclusion of diverse perspectives in design. Ongoing research will evaluate the workshop's impact on integrating these insights into coursework and enhancing the sense of belonging in engineering programs.
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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 | 0.038 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".