Applying Indigenizing Principles of Decolonizing Methodologies in University Classrooms
Bibliographic record
Abstract
This case study examines ongoing work to Indigenize education programs at one Canadian university. The history of the academy in Canada has been dominated by Western epistemologies, which have devalued Indigenous ways of knowing and set the grounds for continued marginalization of Indigenous students, communities, cultures, and histories. We argue that institutions of higher learning need to move away from the myopic lens used to view education and implement Indigenizing strategies in order to counteract the systemic monopolization of knowledge and communication. Faculties of education are taking a leading role in Canadian universities by hiring Indigenous scholars and incorporating Indigenous ways of knowing into teacher education courses. Inspired by the 25 Indigenous principles outlined by Maōri scholar Linda Tuhiwai Smith (2012), four Indigenous faculty members from Western Canada document effective decolonizing practices for classroom experience, interaction, and learning that reflect Indigenous values and orientations within their teaching practices.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.062 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".