Framework and Methodology for Improved Indigenous-Led Decision-Making on Water and Wastewater Design and Management
Bibliographic record
Abstract
The full participation of Indigenous Peoples within water and wastewater policy and decision-making is hindered by many factors, including inadequate resources, funding, and, overall, a lack of respect or formal recognition of Indigenous rights. Despite significant investments and advancement in technologies to treat water and wastewater, Indigenous communities across Canada continue to face persistent boil-water advisories and inadequate drinking water quality and wastewater treatment. Holistic approaches that address the technical elements, in addition to the social, economic, and demographic elements are needed in order to address the complex issues surrounding the delivery of safe drinking water and adequate wastewater treatment within communities. Greater emphasis on the importance of Indigenous-led management of water and wastewater is needed to increase control and decision-making power around water and wastewater management. This thesis investigates tools and methodologies for enhanced Indigenous-led control and decision-making on water and wastewater management. The research comprises six related subjects: (1) a framework for enhanced Indigenous participation in water and wastewater presented as a tool which can improve Indigenous health and wellbeing; (2) investigation into options to improve engagement with Indigenous peoples on water and wastewater; (3) Indigenous-driven operator training and certification regimes; (4) a decolonized framework to facilitate community water sustainability and security; (5) proposed elements of a National Indigenous water strategy; and, (6) real-time monitoring technology as a tool to improve drinking water as supplied. Through these subjects, Indigenous-centered, collaborative and participatory methodologies and tools for water and wastewater treatment, design and management are presented.
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 | 0.046 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".