Exploring Attitudes Towards Water Collaboration for Source Water Protection Between First Nations and Ontario Municipalities
Bibliographic record
Abstract
My research will examine how collaborative source water protection planning involving First Nations, municipalities, and conservation authorities can act as an avenue for enhancing water security on-reserves in southern Ontario. There is plenty of academic literature that examines the extent of water quality issues on First Nations reserves in Canada, and on the factors that contribute to the problem. However, what is lacking are those focused on collaborative efforts between First Nations, municipalities, and conservation authorities. This gap has been acknowledged by other academics in the field. For example, Nelles and Alcantara (2011) claim scholars have ignored the variety of inter-governmental agreements between Indigenous communities and municipal governments in Canada. “We know very little about collaborative agreements, how or why they have emerged or failed to emerge, and whether or not they would be successful” (Nelles and Alcantara, 2011). Some questions have yet to been answered, such as, what collaborative models currently exist that would enable source water protection? What kind of relationships exist between First Nations and their neighbouring municipalities and conservation authorities? How can these relationships work to positively impact source water protection in the region? The goal of this research is to assess the attitudes, opinions, and experiences of First Nations, Municipalities, and Conservation Authorities in a shared watershed to determine how they might be able to work towards collaborative source water protection planning. A case study approach will be used with COTTFN, the City of London, and Upper and Lower Thames Conservation Authorities. This document will act as a guide to collaborative efforts and relationship building can enhance source water protection.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".