Indigenization as inclusion, reconciliation, and decolonization: navigating the different visions for indigenizing the Canadian Academy
Bibliographic record
Abstract
Following the release of the Truth and Reconciliation Commission of Canada’s Calls to Action, Canadian universities and colleges have felt pressured to indigenize their institutions. What “indigenization” has looked like, however, has varied significantly. Based on the input from an anonymous online survey of 25 Indigenous academics and their allies, we assert that indigenization is a three-part spectrum. On one end is Indigenous inclusion, in the middle reconciliation indigenization, and on the other end decolonial indigenization. We conclude that despite using reconciliatory language, post-secondary institutions in Canada focus predominantly on Indigenous inclusion. We offer two suggestions of policy and praxis— treaty-based decolonial indigenization and resurgence-based decolonial indigenization—to demonstrate a way toward more just Canadian academy.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.067 | 0.113 |
| Scholarly communication | 0.029 | 0.013 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.005 | 0.012 |
| 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".