Framing Diversity and EDI Practices: A Comparison of Strategic Planning and Recruitment Materials in Two Canadian Universities
Bibliographic record
Abstract
Multiculturalism has been official Canadian policy since 1971. However, racial equity from this policy has not resulted as there continues to be persistent educational attainment gaps and underrepresentation among Indigenous and Black Canadians in higher education. Post-secondary credentials have become essential to success in knowledge economies. Given these attainment gaps, the purpose of this study was to explore how postsecondary institutions frame and promote diversity. I conducted a content analysis of strategic planning documents and viewbook recruitment materials from Canada’s two largest universities (University of Toronto and University of British Columbia) and sought out any available student racial composition data—a scarcity in Canada. As expected, I found both universities promoted principles of diversity and equity positively within their materials. Only the University of British Columbia produced some student racial composition data. Reliable student compositional data lends credibility to diversity claims in these documents. Without it, there are serious implications for the enrolment and success for those who have been historically excluded. Without access and success, patterns of inequity in labour market outcomes could be perpetuated.
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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".