Towards sustainable water governance
Bibliographic record
Abstract
The focus of this paper is on multi-loop social learning processes required to move towards more sustainable water governance. Multi-loop social learning is recognized as a crucial element to decision-making involving a process of managing change where the central methodological concern is with effectively engaging the necessary participation of system members in contributing to the collective knowledge for more sustainable policy outcomes. A research framework that provides a deeper understanding of the conditions required for the facilitation of multi-loop social learning is developed to assess the capacity for multi-loop social learning of the Quebec water governance system. This research focuses in particular on six watershed management organizations and their watershed territories. In total, 41 semi-structured interviews were conducted with staff of the watershed management organizations as well as with actors and stakeholders associated with three of the six studied watershed territories. This study shows that although the watershed management organizations have the potential to fulfill a key role in facilitating multi-loop social learning, there are still a number of challenges that will need to be addressed to achieve this potential. Limited capacity and perceived credibility of the watershed management organizations and a mismatch between provincial- and local-level discourses, as well as a limited participation of a wide diversity of actors and stakeholders across different levels and scales, are examples of conditions that hinder the facilitation of multi-loop social learning in Quebec. Opportunities to overcome some of these key challenges include renewing partnerships with stakeholders that have so far not been involved by exploring innovative tools for knowledge co-production, and accelerating the transition towards a more collaborative water governance system through the development of required traits and skills of current and future change makers and leaders.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".