Indigenous Principles Decolonizing Teacher Education: What We Have Learned
Bibliographic record
Abstract
Although teacher education programs across the country are currently under significant review and reform, little attention is paid to the importance of Indigenous principles that could inform or transform them. Attention to Indigenous principles such as those presented in this paper can, we believe, serve to decolonize teacher education, offering programs that enable greater success for a wider array of diverse students, both Aboriginal and non-Aboriginal, and address their needs and interests. The intent of this paper is to draw attention to the ways Indigenous principles offered by Lil’wat scholar Lorna Williams have influenced one teacher education program, and to share some of the ways that these principles have been enacted within the program. We offer our perspectives as narrative accounts of what we have done in our courses and in our teacher education program that reflect the principles explained in the paper. We do not feel we can express this perspective any different other than to recount shifts made and our observations as educators. These could be expressed as case studies but this would only be paying lip service to claiming a methodology that was not really followed. We offer this paper more as a sharing of narratives drawn to the indigenous principles. Authenticity comes from our common perceptions from different perspectives in the program.Keywords: Indigenous Knowledge; Teacher Education; Decolonization
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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 | 0.017 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".