Quantifiable progress of the First Nations Water Management Strategy, 2001–2013: Ready for regulation?
Bibliographic record
Abstract
Drinking water security is a serious issue for many First Nations reserve communities in Canada. Over the last decade, CAD $2 billion has been invested to improve the situation by way of several key policies. Though action plans have been developed, expert panels have been struck and commissioned assessments have occurred, little progress has been reported, and on-reserve communities suffering through drinking water emergencies continue to be featured in the media. This paper presents an evidence-based critical analysis of federal policies related to drinking water on First Nations lands, and their associated follow-up progress reports and commissioned assessments. The goals and outcomes of policies since 2001 are noted, and the scope and outcomes of each are compared. This study uses an exploratory analysis of government-documented quantifiable indicators to assess the progress made through the implementation of varied policies and expert panel recommendations. The analysis highlights shortfalls in the collection of indicator data that show that communities have the technical capacities to meet policy requirements. The effectiveness of government policies to prepare communities for the imposition of regulations introduced through the passing of Bill S-8, The Safe Drinking Water for First Nations Act (2012), is discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".