Indigenizing and Decolonizing the Teaching of Psychology: Reflections on the Role of the Non‐Indigenous Ally
Bibliographic record
Abstract
Canada's 2015 Truth and Reconciliation Commission published 94 Calls to Action including direction to post-secondary institutions "to integrate Indigenous knowledge and teaching methods into classrooms" as well as to "build student capacity for intercultural understanding, empathy, and mutual respect." In response, Canadian universities have rushed to "Indigenize" and are now competing to hire Indigenous faculty, from a limited pool of applicants. However, it is missing the true spirit of reconciliation for non-Indigenous faculty to continue with the status quo while assigning the sole responsibility of Indigenizing curriculum to these new hires. How can non-Indigenous psychology professors change their teaching to ensure that all students acquire an appreciation of traditional Indigenous knowledge about holistic health and healing practices, as well as an understanding of Canada's history of racist colonization practices and its intergenerational effects? Community psychologists, particularly those who have established relationships with Indigenous communities, have an important role to play. In this article, I survey the existing literature on Indigenizing and decolonizing psychological curriculum and share ways in which I have integrated Indigenous content into my psychology courses. I also reflect upon the successes, questions, and ongoing challenges that have emerged as I worked in collaboration with first Anisinaabek First Nations and then Mi'kmaw/L'nu First Nations.
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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 | 0.019 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.047 | 0.090 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 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".