Deliberative democracy in Canadian watershed governance
Bibliographic record
Abstract
Bottom-up watershed governance that features citizen engagement in decision-making is touted as a panacea for better social and environmental outcomes. However, there is limited agreement on how exactly this engagement occurs, and how it can be assessed. Water decision-making may result in better social outcomes when decision-making is deliberative and democratic. This article brings together a cross-disciplinary framework to assess deliberative democratic practices in local water councils (LWCs) in the Prairie Provinces, Canada. We apply this framework to assess and compare LWCS, using data from a review of secondary sources and semistructured qualitative interviews with members of LWCs. Our framework was useful for identifying strengths and shortcomings of deliberative democracy within and across LWCs. The strengths of the Manitoba model are its significant mandate and stable tax funding. Alberta’s strengths are in the areas of community representation and significant contested deliberation. Saskatchewan’s strengths are its interconnectedness with other organisations, sectors, and governments. While LWCs have made important contributions to local watershed governance, a consideration and comparison of deliberative democratic practices offers options for policy change strengthening the deliberative democratic practices of LWCs.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.027 | 0.027 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 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".