Drinking Water Consumption Patterns: An Exploration of Risk Perception and Governance in Two First Nations Communities
Bibliographic record
Abstract
Many Indigenous communities across Canada suffer from the lack of access to clean drinking water; ensuring individuals and communities have safe water to drink either from their home or from their local environment requires the consideration of multiple factors including individual risk perception. In collaboration with local leaders, semi-structured interviews (n = 99) were conducted over a two-year period in the Dene Tha’ First Nation and Kátł’odeeche First Nation to unpack the issue of risk perception and its meaning to local community members. These local metrics of risk perception including smell, taste, safety, health fears and level of concern were then used to explore patterns in other data on drinking water consumption patterns and bottled water use. The results are consistent with previous research related to water insecurity and indicate that both communities consume more bottled water than the average Canadian. Results also varied by jurisdiction; those in Alberta indicated much higher levels of concern and a greater degree of bottled water consumption.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".