How Does Adaptive Governance Help Restore and Protect Shared Waters?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
When watersheds span multiple administrative jurisdictions, ensuring the equitable division of responsibility, conflict resolutions and information sharing are all needed to achieve ecological balance, economic development, and social security. Under socio-ecological conditions full of uncertainties, diverse participating groups and multiple perspectives on resource threats need to be involved. Adaptive governance as a theory refers to the structures and processes by which people can address successive interventions and optimize governmental decisions. Through reviewing existing research and analyzing case studies, we uncover problems for shared water governance and highlight attributes of good adaptive governance processes. We emphasize the importance of learning, resilience, as well as accountability, and discuss how these features have the potential for building effective governance with adaptive capacity. We propose a conceptual model to help enable and measure the adaptive capacity for shared water governance at regional scale.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it