Rethinking Indigenous Community-Led Water Sustainability: Decolonial and Relational Approaches in Western Canada
Bibliographic record
Abstract
This study examines Indigenous community-led water sustainability in Western Canada through relational and decolonial lenses, addressing the interplay between traditional knowledge, environmental governance, and cultural identity. The relational and decolonial frameworks emphasize water as a living entity integral to environmental sustainability and community well-being, contrasting with extractive Western models that prioritize economic gains. Using a community-led collaborative methodology, the research engaged Elders, Knowledge-keepers, and youth in discussions and land-based activities, reinforcing intergenerational knowledge transfer. The findings showcase critical challenges to Indigenous water governance, including industrial encroachments, climate change, and colonial environmental management systems that marginalize Indigenous perspectives. These human-created challenges threaten Indigenous water quality and disrupt Indigenous sustainable governance, underlining the need for alternative, adaptive frameworks. Despite these challenges, Indigenous communities are reclaiming water sustainability through initiatives that implement traditional knowledge, cultural revitalization, and collaborative governance models. Such efforts emphasize respect, reciprocity, and stewardship, promoting long-term environmental 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".