Mapping Approaches to Decolonizing and Indigenizing the Curriculum at Canadian Universities
Bibliographic record
Abstract
This article identifies five predominant approaches to Indigenizing the curriculum occurring within Canadian universities today. Examining these approaches in relation to theories of change articulated by Gaudry and Lorenz (2018) and Stein (2020), the article considers the possibilities and limits of each approach as well as the degree to which they challenge the colonial and Eurocentric edifices of Canadian universities. While many of the current approaches to curricular change involve minor reforms that focus on individual transformation rather than substantive structural shifts, the authors also identify promising initiatives that push toward greater Indigenous intellectual sovereignty and institutional autonomy. The article concludes by calling on academic institutions to better center Indigenous Peoples, lands and knowledges in curricular change, and more specifically, to embrace structural revision that ensures Indigenous leadership and autonomy.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.025 | 0.021 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".