The “gift” of Indigenous knowledge and critical, place-based curriculum development through ethical relationality
Bibliographic record
Abstract
Formal education in Canada has historically been a tool of cultural assimilation and deep harm. Change in education is needed not only to address historical wrongs and the cultural erasure of Indigenous peoples but also to create meaningful learning spaces for youth faced with numerous kinds of colonially induced social and ecological sustainability crises, such as climate change and freshwater ecosystem insecurity. The integration of Indigenous knowledge has been one means of decolonizing education. This is not a tokenistic process; rather, if mainstream education has any chance of truly being decolonized, Indigenous knowledge must infuse our ways of being and doing, including from the very point of curriculum conception and development. Through a critical, place-based approach, this article highlights the opportunities and challenges of grounding curriculum development in Indigenous knowledge within the existing formal program of studies. Drawing on extensive Indigenous knowledge documented through a larger research project, we co-designed 12 modules focused on questions of social–ecological change in the Mackenzie River Watershed. Working in ethical relationality with Indigenous Elders, leaders, and youth, we grounded the educational modules in Indigenous knowledge, which speaks back to the formal program of studies. We conclude that this approach and process for translating knowledge from research into educational resources, while having some value, is limited in impact.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".