Understanding Local Water Collaboration for the Potential to Enhance Community Source Water Protection at Chippewas of the Thames First Name
Bibliographic record
Abstract
First Nations in Canada are disproportionately affected by chronic drinking water insecurity (Bakker, 2012). Aboriginal Affair and Northern Development Canada conducted an assessment of First Nations water and wastewater systems in 2001 and found significant risk to the quality and safety of drinking water on three- quarters of all systems (Johns and Rasmussen, 2008). Neegan Burnside (2011) classified four differentrisks that affect drinking water systems for First Nations, which include, no source water protection plan,deterioration of water quality over time, risk of contamination, and insufficient capacity to meet futurerequirements. This study found that the two highest risks were risk of source water contamination and thelack of a community source water protection plan (Neegan Burnside, 2011). Water security, sustainableaccess on a watershed basis to adequate quantities of water of acceptable quality to ensure human andecosystem health (Bakker, 2012), therefore requires source water protection and collaboration amongwater actors. Collaboration is defined as the pooling of resources by multiple stakeholders to solveproblems, which includes a balance of power among actors, mutually agreed upon objectives, is perceived as legitimate, and includes a wide variety of stakeholders (Ashlie, 2019; Van Der Porten, 2013; Spencer etal., 2016; Black & McBean, 2017).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".