Weaving knowledge systems to eradicate drinking water crises in First Nations across Canada
Bibliographic record
Abstract
In Canada, First Nations (FN) are the largest of three Indigenous groups who have occupied and lived on the land for thousands of years. With a current population of about 1.1 million, universal access to safe drinking water remains a persistent problem, with advisories a norm rather than an exception in many FN communities. This study examines the Federal Government's approach to resolving the issues of long-term drinking water advisories (LTDWAs) across FN Reserves in Canada. The objective was to determine the acknowledgment and application of FN water principles and values within the federal LTDWA intervention framework. Financial and technical capacity was also explored. Results indicate that the Federal Government's approach to eradicating LTDWA in FN focuses on infrastructure technologies, overlooking other aspects of sustainable water supply, such as advanced source water protection. As such, it overlooked (1) FN water management principles and values; (2) FN strength and capacity to manage their water; and (3) First Nations' right to self-determination. It is argued that the poor attention to FN water principles and values, including the failure to address the issues of financial and technological capacity, undermines FN rights to self-determination and contributes to the continuous presence of LTDWAs in communities.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".