Environmental Governance for the Anthropocene? Social-Ecological Systems, Resilience, and Collaborative Learning
Bibliographic record
Abstract
The Anthropocene is characterized by rapid global change, necessitating adaptive governance. But how can such adaptive governance be operationalized? The article offers a three-point argument to approach this question. First, people and environment need to be considered together, as social (human) and ecological (biophysical) subsystems are linked by mutual feedbacks, and are interdependent and co-evolutionary. These integrated systems of humans and environment (social-ecological systems) provide an appropriate unit of analysis. Second, the resilience approach deals with change in multilevel complex systems, and has stimulated much of the adaptive governance literature by addressing uncertainty and adaptation to unforeseen future changes. Third, there is a need to foster collaborative approaches to improve social and institutional learning, as for example in adaptive management, collaborative learning networks, and knowledge co-production. Collaborative learning is perhaps where further research, experimentation, and application might make a difference for operationalizing adaptive governance, with a focus on institutions, at all levels from local to international.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".