Evaluating collaborative approaches to governance for water allocation in Canada: Lessons from Ontario
Bibliographic record
Abstract
Collaborative approaches to environmental governance are becoming commonplace around the western world. In Canada, all jurisdictions are using various forms of collaboration to address water issues. With few exceptions, the collaborative processes address problems that exist in whole or in part in rural areas. Thus, the agriculture sector is a critical participant. This certainly is the case in Ontario, especially in the case of collaborative processes designed to address low water conditions and droughts. The purpose of this research was to evaluate the effectiveness and appropriateness of collaborative approaches to dealing with water scarcity and conflicting demands for water. The Province of Ontario provided the institutional setting for the study. We were particularly concerned with the extent to which collaboration provides an effective and appropriate basis for water sharing in cases where agriculture is a prominent user. This led us to a focus on the Ontario Low Water Response (LWR) program. Ontario's Low Water Response program is the primary vehicle through which water shortages and droughts are addressed in the province. The program's overall functioning and effectiveness have been studied previously, but little or no attention has been given to understanding the extent to which this collaborative has produced outcomes that have been protected by the provincial government. This is a particularly important concern because the Province of Ontario, through the Ontario Ministry of the Environment (and Climate Change) has ultimate authority for dealing with water shortages through its Permit to Take Water Program. Experiences from around the world demonstrate that a failure to respect the outcomes of collaborative processes undermines their effectiveness and leads to considerable dissatisfaction. At the same time, from the perspective of democratic legitimacy, the province remains accountable. All jurisdictions are struggling to resolve the tension between these two objectives.
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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 | 0.027 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.018 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".