Transformative Sustainability Education and Empowerment Practice on Indigenous Lands
Bibliographic record
Abstract
Set against a context of Indigenous health disparities, climate turmoil, and unpredictability of human-ecological systems, this article asks the question of how transformative sustainability education (TSE) with its increasing emphasis on Indigenous knowledge and ways of being can be effectively and ethically applied in colonized modern nation-states? In doing so, it makes the necessary links between the interconnected goals of addressing underlying determinants of Indigenous health and supporting the resurgence of Indigenous knowledges and ways of being toward ensuring planetary well-being more generally. As a means of negotiating this critical interface, three pedagogical capabilities of TSE (Scaling DEEP, Scaling OUT, and Scaling UP) are briefly outlined. Having laid this theoretical groundwork, this article (Part 1) focuses primarily on the role of the transformative sustainability educator in Scaling DEEP (effecting cultural and relational transformation from a de-colonial perspective) as a necessary precursor to the interrelated domains of Scaling OUT and UP (programming and policy change).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".