Affirmative action and education equity in higher education in the United States and Canada
Bibliographic record
Abstract
In recent years, many of the most contentious debates regarding affirmative action in the United States have taken place on the terrain of universities. Numerous court decisions have examined the extent to which university admissions policies may accord preferences to students from underrepresented and racialized groups. There has been much less litigation in the Canadian context; however, the underlying issue of how to insure equality and inclusion in the face of systemic discrimination is a critical concern. Therefore, the purpose of this thesis is to explore the key legal developments regarding affirmative action initiatives in higher education in Canada and the United States, by highlighting the important differences in the justifications. This thesis will advance and explore two divergent rationales that emerge as salient in affirmative action programs in the United States and Canada. In the American context, diversity is adopted as the main justification for affirmative action programs in universities, as opposed to the Canadian context where it is 'ameliorating the conditions of disadvantaged groups,' that is employed as the key justification for what are called education equity programs. In looking at the experience of each country, this thesis will also examine some of the reasons and ways in which we can understand the different approaches and justifications that have emerged in Canada and the United States in the affirmative action debate.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.040 | 0.029 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| 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".