Exploring Water Governance and Management in Oneida Nation of the Thames (Ontario, Canada): An Application of the Institutional Analysis and Development Framework
Bibliographic record
Abstract
Water is vital to Canada’s First Nations peoples. Despite the significance of water and ongoing efforts by various actors in Canada to make improvements, the conditions of drinking water safety are a persistent concern and deplorable in many First Nation communities. This research explores water institutions and their influence on water governance and management in a First Nations context. Oneida Nation of the Thames, located in southern Ontario, is the specific case investigated. This community has drinking water concerns and a myriad of institutions relating to water governance and management. The Institutional Analysis and Development (IAD) framework guided the exploration in Oneida. Water institutions (formal and informal) are identified and analysed in terms of exogenous factors, the action arena, patterns of interaction, and outcomes of these interactions. An evaluation of institutional performance in relation to water governance and management is offered. Gaining insights about how institutions guide the behavior of people involved in water governance and management in Oneida highlights the need to consider their influences in other First Nation communities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".