Non-Indigenous Instructors Teaching about Indigenous Content: Reflections and Recommendations from Indigenous Ways of Knowing and Pedagogy
Bibliographic record
Abstract
This article takes a scholarship of teaching and learning approach to improve the authors teaching about Indige-nous content as non-Indigenous teacher educators. It explores how they attempted to incorporate Indigenous content and teaching practices into multicultural education classes and then reflect on how they could have improved their teaching practice. Both authors provide their unique positionality which provides context which is essential to consider when doing equity-based work such as teaching about/with Indigenous communities. The authors discuss their teaching experiences after they occurred with one another and then engage in an exploration via literature on teaching about Indigenous content. The outcomes of two years of co-reflection and analysis of the literature are shared in this article in hopes to help guide both the authors and other non-Indigenous instructors on how to improve their teaching and learning about Indigenous content in courses. The findings stress the importance of (1) acknowledging land as a conduit for domination, (2) recognizing all who teach us, and (3) Indigenous guest lecturers and intergenerational learning.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| 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".