The Policy Efforts to Address Racism and Discrimination in Higher Education Institutions: The Case of Canada
Bibliographic record
Abstract
This paper reviews existing policies related to anti-racism and anti-discrimination at five major universities in Canada and assesses the equity initiatives undertaken by university authorities to promote greater access and inclusion of different ethnic minority groups. The study is based on secondary data sources. Therefore, policy papers, documents, study reports available in those universities, government policy and legislation, journals, and similar were consulted to construct the piece. Findings reveal that although the universities have some sort of anti-racism and anti-discrimination policies to combat racism and discrimination in their educational setting, they face challenges or limitations in adopting holistic and inclusive measures for the different ethnic and diverse minority groups studying there. The study argued for promoting discussions and responses to specific policies, programmes, and practices, including behaviours and attitudes in the institutional and professional contexts, for combating racism and discrimination. The findings may be helpful for academics, policymakers, and administrators to develop their understanding of institutional racism, identify challenges, and adopt policy measures to address it.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.044 | 0.011 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".