Beyond Physical: Social Dimensions of the Water Crisis on Canada’s First Nations and Considerations for Governance
Bibliographic record
Abstract
The provision of safe drinking water is one of the most pressing health issues facing First Nations communities in Canada. Despite numerous government assessments, training initiatives, and billions of dollars in targeted funding, access to safe drinking water continues to be a perennial problem in communities nationwide. The water crisis in these communities, characterized by poor access to safe, sustainable, and reliable drinking water sources, is greater than that perceived by the alarming incidence reports of physical illness, waterborne infections and boil water advisories. A host of issues related to inequity, justice, and institutional trends are reflective of, and contribute to, an erosion of the social, cultural and spiritual health of First Nations in Canada. We seek to provide an overview of the social dimensions of the water crisis. By situating the issues in this broader context, we hope to provide the basis for greater dialogue and depth of study into the socio-cultural implications of the water crisis. If tenable solutions to the myriad of water issues affecting First Nations people in Canada are to be realized, an acknowledgement and understanding of the historical, cultural, social, political, economic and legal framework that have steered our arrival at the current situation must be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.039 | 0.031 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".