Understanding Local Water Collaboration for the Potential to Enhance Community Source Water Protection at Chippewas of the Thames First Nation
Bibliographic record
Abstract
First Nations in Canada are disproportionately affected by chronic drinking water insecurity (Bakker, 2012). In a 2011 National Assessment of water and wastewater systems in First Nations communities, Neegan Burnside (2011) found the two highest risks to drinking water were: risk of source water contamination, and lack of a community source water protection plan (Neegan Burnside, 2011). Within a watershed context, the influence of upstream water activities can impact the quality of source water for downstream drinking water systems. Inland water management in Ontario is a shared responsibility primarily between the province, conservation authorities, municipalities and First Nations. Community-level water security therefore is broader than on-reserve water management. For those communities located downstream in the watershed, water security requires collaboration with upstream water actors. Using a case study approach with the Chippewas of the Thames First Nations in southwestern Ontario, my research seeks to understand how collaboration between local water actors can support First Nations community-level source water protection. This presentation will address Objective 2 of my MSc thesis: to understand the attitudes, opinions, and experiences of First Nations, conservation authorities, and municipalities as it relates to water collaboration. I highlight results that include the meaning of collaboration to First Nations and water actors, the impact of provincial and federal policy on collaboration, the key challenges to watershed-based collaboration, and the opportunities for future water collaboration that supports community source water protection planning.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".