Understanding and applying principles of social cognition and decision making in adaptive environmental governance
Bibliographic record
Abstract
Environmental governance systems are under greater pressure to adapt and to cope with increased social and ecological uncertainty from stressors like climate change. We review principles of social cognition and decision making that shape and constrain how environmental governance systems adapt. We focus primarily on the interplay between key decision makers in society and legal systems. We argue that adaptive governance must overcome three cooperative dilemmas to facilitate adaptation: (1) encouraging collaborative problem solving, (2) garnering social acceptance and commitment, and (3) cultivating a culture of trust and tolerance for change and uncertainty. However, to do so governance systems must cope with biases in people's decision making that cloud their judgment and create conflict. These systems must also satisfy people's fundamental needs for self-determination, fairness, and security, ensuring that changes to environmental governance are perceived as legitimate, trustworthy, and acceptable. We discuss the implications of these principles for common governance solutions (e.g., public participation, enforcement) and conclude with methodological recommendations. We outline how scholars can investigate the social cognitive principles involved in cases of adaptive governance.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.045 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| 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".