Decolonizing Sustainability through Indigenization in Canadian Post-Secondary Institutions
Bibliographic record
Abstract
Sustainability discourse indicates a need to reconsider our approaches to social, economic, and environmental issues because, without deep transformation, global human survival is in jeopardy. At the same time, post-secondary education institutions in Canada are Indigenizing their settings but have rarely taken up sustainability and Indigenization as related concepts. In this research, participants delivering Indigenous programming in ten colleges and universities across Canada contributed their insights on the relationships between Indigenous worldviews and sustainability in their territories and institutions. The five key findings that emerged from the study are: (1) Indigenous worldviews are based on a belief in the sacred, which orients Indigenous knowledges and responsibilities for sustaining life on Earth; (2) sustainability is expressed as a function of tradition linking Indigenous identity with culture, language, and environmental health; (3) entrenching Indigenous knowledges throughout institutions is to sustain cultural identity; (4) national and international standards supporting Indigenous self-determination are primary drivers for the inclusion of Indigenous knowledges and advance the underlying principle of sustainability; and (5) Indigenous holistic learning includes social, economic, and environmental aspects of sustainability.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".