Placing environmental democracy at the heart of co-governance: rethinking the governance of complex waterways
Bibliographic record
Abstract
The ways in which complex systems are governed often generate democratic malaise due to a gap between people’s expectations and the ability of authorities to meet these expectations. Additionally, misalignment between ecological and social systems makes it difficult for decision-makers to adapt to dynamic environments. Co-governance, which involves collaboration and engagement between governments and a range of stakeholders, is a common mechanism to address these challenges. However, co-governance has been criticized for being exclusionary, ad hoc, and counter to democratic principles. We argue that principles of environmental democracy offer a way to strengthen the effectiveness and legitimacy of co-governance. We base this argument on findings from research on the governance of national historic waterways in Ontario, Canada, specifically the Rideau Canal and Trent-Severn Waterway. These waterways reach across multiple watersheds, territories, and jurisdictions, involving a range of authorities and different stakeholder groups, many of whom have expressed dissatisfaction with current decision-making structures. By examining the difficulties involved in governing these waterways, we propose three reforms to co-governance that would enhance environmental democracy: (1) tiered-mechanisms that facilitate collaborative governance, (2) targeted collaborative exercises to create and strengthen within-ties across stakeholder groups, and (3) semi-regular forums to support communication among stakeholder groups.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".