The Multiple Barrier Approach to Safe Drinking Water for First Nations Communities: A Case Study
Bibliographic record
Abstract
The drinking water contamination tragedy in Walkerton, Ontario during the spring of 2000 led to many changes in water management for the province. Among these changes has been the increased use of the multiple barrier approach (MBA) to safe drinking water as the basis of water management for communities throughout Ontario. The MBA is also used in the management of water for First Nations communities throughout Ontario and Canada. Literature on water quality management for First Nations suggests that despite these changes, many communities continue to face challenges for ensuring the safety and quality of their drinking water supplies. Fort William First Nation, Gull Bay First Nation, and Mattagami First Nation, were selected for this study in order to investigate the use of the MBA in these communities. Data was collected using key informant interviews with representatives of institutions that affect water management for the case study communities, direct observations during visits to two of the communities and attendance at a First Nations water policy forum, and through a review of recent reports and publications on safe drinking water for First Nations. The research has provided insight into the challenges that the case study communities face for ensuring safe drinking water under the MBA, as well as opportunities that exist to address those challenges. The findings suggest that the MBA currently does not meet the unique needs of some First Nations communities. They also suggest that specific adaptations of existing water management strategies to the MBA framework may lead to a more effective approach to ensure safe drinking water for First Nations communities. This thesis focuses on several key ways to make these changes: Strengthen public involvement and awareness; Introduce effective legislative and policy frameworks; Encourage research, science and technology for First Nations’ water management; Allocate sufficient financial resources to First Nations to recruit, train and retain qualified water managers and maintain drinking water infrastructure, and; Increase efforts to ensure that water management goals are supported by local and indigenous traditional knowledge, beliefs and perspectives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".