Evolutionary Perspectives on Environmental Governance: Strategy and the Co-Construction of Governance, Community, and Environment
Bibliographic record
Abstract
The attention to sustainability transformations and related processes of learning, innovation, and adaptation has inspired a growing interest in theories that help to grasp the processes of change in governance. This perspective paper and the Special Issue of which it is part explore how evolutionary perspectives on environmental governance can enrich our understanding of the possibilities and limits of environmental policy and planning. The aim of this paper is to highlight some key notions for an evolutionary understanding of governance theory and to show how such an evolutionary perspective can help to develop a more integrated perspective on environmental governance in which the temporal dimension and the effects of steering attempts play a pivotal role. It is argued that the effects of environmental governance on the material environment, community, and governance itself must be considered in their interrelation. Such insight in couplings and co-evolutions can be of great value in the everyday practice of environmental policy and governance and even more so when attempting to transform the governance system towards more ambitious and coordinated goals.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".