A multi-level perspective on the legitimacy of collaborative water governance in Québec
Bibliographic record
Abstract
Collaborative governance entails a shift in emphasis from government control toward voluntary arrangements directly involving non-state stakeholders in decision-making. However, the sources of legitimacy for novel institutions such as collaborative water governance remain unclear. Three levels of decision-making that are highly relevant to understanding legitimacy in these contexts are identified and used to assess the legitimacy of collaborative water governance in Québec, Canada. Using Beetham’s dimensions of legitimacy – legality, justification and consent – the sources and deficits of legitimacy are identified through 35 in-person interviews with local stakeholders, watershed organization staff and provincial policy-makers. Findings illustrate the diverse sources of legitimacy, but also a disconnect between what constitutes legitimacy according to local stakeholders and the provincial government. Deficits in legitimacy include lack of implementation, misfit between collaborative governance and existing representative government, and lack of consent from the Québec government. However, although collaborative governance may not be appropriate for all contexts, it addresses important social needs that government cannot and therefore has high potential to complement the roles of existing institutions. Providing several novel insights, this multi-level perspective illustrates how legitimacy can be used to understand the challenges of complex sociological phenomena such as collaborative governance.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".