Ontario University EDI Strategic Plan Analysis: Power Embedded in Policy Discourse
Bibliographic record
Abstract
This Major Research Project (MRP) in the York University Graduate Program in Education (MEd) explores the policy and action planning discourse in terms of Equity, Diversity, and Inclusion (EDI) at the top five most populated undergraduate universities in Ontario. The potential impact of this research is significant, as EDI policies are crucial for fostering inclusive academic environments, yet their implementation and impact vary widely across institutions. This research aims to explore how universities articulate their commitment to EDI through policy documents and the extent to which these policies address the needs of Black student\npopulations.\n\nThis project identifies the educational attainment gap and underrepresentation among Black Canadians in higher education (HE), as well as how EDI practices and policies (PSE) are addressing racial inequity in Ontario during the last decade. Common trends include a focus on fostering a sense of belonging and addressing systemic barriers, while notable gaps exist in the areas of policy evaluation and accountability. The MRP applies discourse analysis as the primary methodological approach to understanding the power embedded in Ontario university's EDI action planning documents.\n\nCritical discourse analysis is a powerful tool for examining how language, terminology, and racial identity reflect and refract power structures and values within an institution. This research adopts several theoretical frameworks as the guiding methodology to contextualize university commitment to impactful and accountable EDI. The MRPs' contribution to the field of policy discourse and EDI in higher education provide a nuanced understanding of how policy discourse shapes institutional commitments to equity, diversity, and inclusion, specifically for Black students and Ontarians, identifying gaps and inconsistencies in current EDI action planning documents.
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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 | 0.039 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.019 | 0.022 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".