Governing Water in Canada: The Legislative Experiments in New Governance
Bibliographic record
Abstract
Governing water in Canada is in transition. Since 2000, episodes of drought, unsafe drinking water, and polluted watersheds have affected local and First Nations communities. In reaction to these crises, provincial regulators entered a new governance phase. This regulatory turn profoundly transforms the traditional environmental regulatory approach by introducing a collaborative new governance arrangement. The legal scholarship is generally supportive of this trend, however, a dearth of empirical research exists to understand how decisions are made under this new regulatory approach.\nThis dissertation presents an eco-resiliency framework to examine the responsiveness of this new governance mode to environmental change. The primary research question is: What lessons can be taken from resiliency theory and applied in the sphere of environmental regulation and governance? Three comparative case studies of local watershed-level committees in Ontario, Alberta, and the Yukon served as empirical evidence. The research methodology adopted a qualitative approach (i.e., participant observation and interviews with committee members) and a thorough review of the relevant legislation, administrative decisions, policy documents, and media reports. The data was analyzed in terms of the four eco-resiliency elements: flexibility, diversity, a broad perspective, and emergent change.\nContrary to the themes of inclusivity and consensus found in the collaborative governance literature, the research findings exposed an insular and technocratic decision-making process that served the political interests of the province and the administrative needs of the regulatory agency. Even though, in theory, the provincial regimes under study allowed for a diverse number of stakeholders at the policy table, in practice, only a few experts influenced the decision-making. Local communities ecological health and environmental concerns including First Nations ways of knowing water were overlooked. The devolution of water governance to a local level, rather than empowering local public-interest representatives, concentrates power in the hands of a few participants. Surprisingly, the Yukon Water Board, an administrative tribunal with strict procedural requirements, offered the strongest opportunity for Aboriginal and conservation groups to raise their water concerns. The most important finding is the erosion of the environmental protection function of the state, which is obscured by this policy drift.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.036 | 0.030 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".