Last In Line For Clean Drinking Water Canadaâ s Attempt To Utilize "Comparability" To Address Drinking Water On First Nations Reserves
Bibliographic record
Abstract
Above all, it is time to listen to First Nations communities, leaders and organizations to hear what they have to say about drinking water quality on reserves. The Government of Canada, through Indigenous and Northern Affairs Canada utilizes a comparability policy that determines First Nations community drinking water requirements based upon non-First Nations towns and villages that are nearby. Given the centuries of colonization, degradation from residential school, as well as the current crises involving not just drinking water-related gastro-intestinal and skin diseases but loss of life due to youth suicide in northern communities, a new approach towards drinking water quality and community wellness must be implemented. High on the list would be enacting regulations to protect drinking water quality on reserves. Incorporating First Nations' perspectives is the crucial part of the puzzle that is missing. Through a review of legislative and policy documents, First Nations submissions and position papers and information elicited from key interviews with topic experts, this paper hopes to pull apart the flawed concept of comparability and instead invite the Government of Canada to join with their partners, First Nations, and develop the kinds of drinking water strategies that will bring meaningful change to reserves and restore the human rights of First Nations living in this land base now called Canada.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".