Water is Medicine: Reimagining Water Security through Tr’ondëk Hwëch’in Relationships to Treated and Traditional Water Sources in Yukon, Canada
Bibliographic record
Abstract
There is growing acknowledgement that the material dimensions of water security alone are inadequate; we also need to engage with a broader set of hydrosocial relationships. Indeed, more holistic approaches are needed to explain Indigenous peoples’ relationships to water including the use of traditional water sources such as mountain creeks and springs. In this paper, we seek to reimagine water security through a case study of Tr’ondëk Hwëch’in’s relationships to both treated and traditional water sources throughout the First Nation’s traditional territory in Yukon, Canada. Through community-based research including interviews with Elders and other community members, we examine the importance of traditional water sources for meeting important health requirements including physical, spiritual and cultural wellbeing. This intervention contributes to ongoing debates about what it means to secure safe and affordable water in three key ways: First, we argue that Indigenous water relations invite a shift towards more a holistic understanding of water security; second, we contend that settler colonial politics should be understood as a root cause of water insecurity; finally, we explore how Two-Eyed Seeing can be applied as an alternative to the ‘integration’ of Western scientific and Indigenous approaches to drinking water.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".