Decolonising Canadian water governance: lessons from Indigenous case studies
Bibliographic record
Abstract
Meaningful lessons about decolonising water infrastructure (social, economic and political) can be learned if we scrutinise existing governance principles such as the ones provided by the Organisation for Economic Cooperation and Development in 2021’s Principles on Water Governance. Instead of using only Western frameworks to think about policy within Indigenous spheres of water, sanitation and hygiene, the Government of Canada can look to Indigenous ways of knowing to complement their understanding of how to govern areas of water, sanitation and hygiene efficiently. In this paper, the term Indigenous encompasses First Nations, Inuit and Métis populations. This paper is presented as a step out of many towards decolonising water governance in Canada, and is intended to show that it is necessary to make space for other voices in water governance. By highlighting the dangers in the case studies, three lessons are apparent: (1) there needs to be an addition of Indigenous Two-Eyed Seeing in water governance; (2) Canada must strengthen its nation-to-nation praxis with Indigenous communities; and (3) there needs to be a creation of space in water, sanitation and hygiene that fosters Indigenous voices. This is necessary such that there can be equal participation in policy conversations to mitigate existing problems and explore new possibilities.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.035 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".