‘That water out there is no damn good for anybody’: Experiences with declining water quality in a First Nation community
Bibliographic record
Abstract
In many Indigenous communities, the wellbeing of waterways correlates to the health of the population that it supports. However, current laws and water governance systems often fail to protect water sources and jeopardizes health and wellbeing, particularly in Indigenous communities. This study, curated by an Anishinaabe First Nations community located in Ontario on the Lake of the Woods (LOTW), was designed to detail the varying impacts of adverse water quality on people in the community. A community-based participatory research approach included interviews with Elders and key informants to understand lived experiences of adverse water quality, sources of pollution, and individual and community impacts. Key findings revealed changes in water quality within and between years, with water quality degrading over time. Further, changes in water quality were associated with changes in the community’s health, food sources, and activities. Finally, a paternalistic colonial history between Indigenous people and the Government of Canada continues to resonate and cause strained jurisdictional relations between the two groups. Opportunities and future water stewardship strategies require the active participation and inclusion of Indigenous people in policymaking, programming, and water management. As proposed by the LOTW community, this includes improving water quality monitoring, upgrading septic systems in the community, reintroducing wild rice to the shorelines, and creating water activities programming for Indigenous youth.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.021 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| 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".