Diversity Research in an Engineering Technology Program: Promising Practices for Diversity Research Initiatives in Post-secondary Education
Bibliographic record
Abstract
While efforts are underway to solve gender disparity in engineering, there tends to be a focus on gender at the expense of other diversity considerations. Few Canadian universities collect data about their racialized student population despite human rights advocates and the government of Ontario endorsing such an approach to uncover inequality and better understand the needs of Canada’s growing diverse student population. Of those universities that collect demographic information on their student body, few studies dig deep enough to understand how students’ identities affect their learning experience. The proposed study goes further to understand faculty, staff and student experiences around teaching and learning. This study views educational experiences holistically (within and outside the classroom) in order to understand how participants face discrimination or exclusion. In this paper, the authors provide an overview of demographic surveys at Canadian universities and describe McMaster University’s recent work in this area. The authors then provide a study overview and our intended next steps. It is the hope that this research, and affiliated workshops, will help faculty and staff better understand the lived experiences of discrimination that our students face, and may help illuminate bias and barriers within our educational offerings that go unnoticed
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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 | 0.191 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.065 | 0.028 |
| Scholarly communication | 0.033 | 0.020 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".