Multi-year study of sense of belonging at a large comprehensive Ontario university
Bibliographic record
Abstract
Efforts to diversify engineering educational spaces have seen some success in recent years, however minority groups continue to have different experiences during their studies both in university environments and external job placements. Online surveys were developed and delivered in the 2022/23 and 2024/25 academic years investigating how undergraduate and graduate students experience their engineering education with an emphasis on the perceived experiences of equity seeking groups (ESG). A total of 921 responses were gathered between the two years with ~70% of respondents being men. Clear differences were noted in responses from students of different genders. Additional 2024/25 survey questions further investigated the presence of engineering role models prior to university, what students perceive to be challenges for equity seeking groups, and how belonging to the 2SLGBTQ+ community impacts responses. Results indicate that students from ESG are more aware of challenges related to these identities compared to students with non-minority identities.
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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 | 0.000 | 0.000 |
| 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".