Legitimacy assessment throughout the life of collaborative water governance
Bibliographic record
Abstract
Abstract Collaborative governance arrangements involving a diversity of groups (e.g., governments, civil society, industry) are increasingly being used to make decisions or give advice to decision‐makers on water issues. Legitimacy is a critical factor for the effectiveness, efficiency, stability, and popular approval of collaborative efforts. However, as a concept, legitimacy remains contested with various meanings, theoretical backgrounds, and source norms. Clarity is particularly needed around the changing sources of legitimacy as collaborative efforts mature. Drawing on case‐study research of five collaborations in British Columbia, Canada, we present a framework of legitimacy sources as collaborations evolve. Legitimacy during the establishment of a collaboration depends principally on community readiness to collaborate, a sense of need, and the perceived potential for goal achievement. As a collaboration continues to grow, its legitimacy forms mainly from normative processes—the perceived quality of factors such as accountability, transparency, consensus‐building, and representation of relevant discourses. Once a collaboration reaches maturity and faces questions of its future existence, legitimacy is largely result‐based—tangible and contextually meaningful outcomes must be easily identifiable and promoted. Increased understanding of the dynamics of legitimacy can help collaborative efforts strategically plan and work toward their goals as they evolve.
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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 | 0.035 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.010 | 0.017 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| 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".