Shifting the Framework of Canadian Water Governance through Indigenous Research Methods: Acknowledging the Past with an Eye on the Future
Bibliographic record
Abstract
First Nations communities in Canada are disproportionately affected by poor water quality. As one example, many communities have been living under boil water advisories for decades, but government interventions to date have had limited impact. This paper examines the importance of using Indigenous research methodologies to address current water issues affecting First Nations. The work is part of larger project applying decolonizing methodologies to Indigenous water governance. Because Indigenous epistemologies are a central component of Indigenous research methods, our analysis begins with presenting a theoretical framework for understanding Indigenous water relations. We then consider three cases of innovative Indigenous research initiatives that demonstrate how water research and policy initiatives can adopt a more Indigenous-centered approach in practice. Cases include (1) an Indigenous Community-Based Health Research Lab that follows a two-eyed seeing philosophy (Saskatchewan); (2) water policy research that uses collective knowledge sharing frameworks to facilitate respectful, non-extractive conversations among Elders and traditional knowledge holders (Ontario); and (3) a long-term community-based research initiative on decolonizing water that is practicing reciprocal learning methodologies (British Columbia, Alberta). By establishing new water governance frameworks informed by Indigenous research methods, the authors hope to promote innovative, adaptable solutions, rooted in Indigenous epistemologies.
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 | 0.048 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.030 | 0.058 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| 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".