Policy Analysis of Equity, Diversity and Inclusion Strategies in Canadian Universities – How Far Have We Come?
Bibliographic record
Abstract
Institutional efforts to address equity, diversity and inclusion in educational settings have been often met with overwhelmingly critical accounts pointing towards well-intentioned attempts that have reinforced exclusion and inequity. A new wave of recent developments among Canadian research-intensive universities (U15) is providing a slightly different account of universities’ involvement in addressing the needs of equity-seeking students. This paper presents data collected through policy analysis of 50 strategic documents from 15 Canadian universities from 2011-2018. The findings suggest that equity, diversity and inclusion activities have become a policy priority attached to a variety of institutional action plans and performance reports. As a result, there has been an increase in institutional strategic activities including institutional political commitment (e.g. new equity offices, new senior administration positions, mandatory training), student and faculty recruitment with programmatic and research supports (e.g. diversity admission policies, scholarships, access programs, curriculum changes), accompanied by broader efforts to create supportive institutional climates (e.g. student advisors, awards, celebrations). Inconsistencies emerged amongst how equity is defined in policy documents, resulting in either redistributive or inclusive practices in equity, diversity, and inclusion initiatives.
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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 | 0.024 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".