Balancing stability and flexibility in adaptive governance: an analysis of tools available in U.S. environmental law
Bibliographic record
Abstract
Adaptive governance must work "on the ground," that is, it must operate through structures and procedures that the people it governs perceive to be legitimate and fair, as well as incorporating processes and substantive goals that are effective in allowing social-ecological systems (SESs) to adapt to climate change and other impacts. To address the continuing and accelerating alterations that climate change is bringing to SESs, adaptive governance generally will require more flexibility than prior governance institutions have often allowed. However, to function as good governance, adaptive governance must pay real attention to the problem of how to balance this increased need for flexibility with continuing governance stability so that it can foster adaptation to change without being perceived or experienced as perpetually destabilizing, disruptive, and unfair. Flexibility and stability serve different purposes in governance, and a variety of tools exist to strike different balances between them while still preserving the governance institution's legitimacy among the people governed. After reviewing those purposes and the implications of climate change for environmental governance, we examine psychological insights into the structuring of adaptive governance and the variety of legal tools available to incorporate those insights into adaptive governance regimes. Because the substantive goals of governance systems will differ among specific systems, we do not purport to comment on what the normative or substantive goals of law should be. Instead, we conclude that attention to process and procedure (including participation), as well as increased use of substantive standards (instead of rules), may allow an increased level of substantive flexibility to operate with legitimacy and fairness, providing the requisite levels of psychological, social, and economic stability needed for communities to adapt successfully to the Anthropocene.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.008 | 0.034 |
| Scholarly communication | 0.018 | 0.021 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| 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".