Legal and institutional foundations of adaptive environmental governance
Bibliographic record
Abstract
Legal and institutional structures fundamentally shape opportunities for adaptive governance of environmental resources at multiple ecological and societal scales. Properties of adaptive governance are widely studied. However, these studies have not resulted in consolidated frameworks for legal and institutional design, limiting our ability to promote adaptation and social-ecological resilience. We develop an overarching framework that describes the current and potential role of law in enabling adaptation. We apply this framework to different social-ecological settings, centers of activity, and scales, illustrating the multidimensional and polycentric nature of water governance. Adaptation typically emerges organically among multiple centers of agency and authority in society as a relatively self-organized or autonomous process marked by innovation, social learning, and political deliberation. This self-directed and emergent process is difficult to create in an exogenous, top-down fashion. However, traditional centers of authority may establish enabling conditions for adaptation using a suite of legal, economic, and democratic tools to legitimize and facilitate self-organization, coordination, and collaboration across scales. The principles outlined here provide preliminary legal and institutional foundations for adaptive environmental governance, which may inform institutional design and guide future scholarship.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".