The role of law in adaptive governance
Bibliographic record
Abstract
The term "governance" encompasses both governmental and nongovernmental participation in collective choice and action. Law dictates the structure, boundaries, rules, and processes within which governmental action takes place, and in doing so becomes one of the focal points for analysis of barriers to adaptation as the effects of climate change are felt. Adaptive governance must therefore contemplate a level of flexibility and evolution in governmental action beyond that currently found in the heavily administrative governments of many democracies. Nevertheless, over time, law itself has proven highly adaptive in western systems of government, evolving to address and even facilitate the emergence of new social norms (such as the rights of women and minorities) or to provide remedies for emerging problems (such as pollution). Thus, there is no question that law can adapt, evolve, and be reformed to make room for adaptive governance. In doing this, not only may barriers be removed, but law may be adjusted to facilitate adaptive governance and to aid in institutionalizing new and emerging approaches to governance. The key is to do so in a way that also enhances legitimacy, accountability, and justice, or else such reforms will never be adopted by democratic societies, or if adopted, will destabilize those societies. By identifying those aspects of the frameworks for adaptive governance reviewed in the introduction to this special feature relevant to the legal system, we present guidelines for evaluating the role of law in environmental governance to identify the ways in which law can be used, adapted, and reformed to facilitate adaptive governance and to do so in a way that enhances the legitimacy of governmental action.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.011 | 0.101 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".