Moving towards Effective First Nations’ Source Water Protection: Barriers, Opportunities, and a Framework
Bibliographic record
Abstract
It is well known that watershed-based source water protection programs are integral to the provision of clean drinking water. However, the involvement of Indigenous communities in these programs is very limited in Canada, which has contributed to the vulnerability of Indigenous source waters to contamination. Through a partnership with an Anishinaabe community, this research aimed to identify challenges and opportunities for communities and practitioners to improve the protection of Indigenous source waters in the province of Ontario. The methodology followed the Indigenous research principles of relationship, respect, relevance, reciprocity, and responsibility. Interviews and a youth focus group were conducted with Indigenous community members and practitioners from industry, academia, non-governmental organizations (NGOs), and government. Analysis was conducted using an iterative process to develop codes and themes in the qualitative data analysis software NVivo. Results indicated that issues with scale, jurisdiction, the concept of source water protection, representation, funding, and capacity impact efforts to protect Indigenous source waters. Hopeful recent developments and upcoming opportunities were identified, and a water protection framework for First Nation communities in Ontario was developed in partnership with an Anishinaabe water protection committee. Recommendations are provided to multiple sectors for moving forward respectfully, and effectively, towards the protection of Indigenous waters.
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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 | 0.036 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.025 | 0.052 |
| Scholarly communication | 0.021 | 0.015 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.006 | 0.010 |
| 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".