Regime shifts and panarchies in regional scale social-ecological water systems
Bibliographic record
Abstract
In this article we summarize histories of nonlinear, complex interactions among societal, legal, and ecosystem dynamics in six North American water basins, as they respond to changing climate. These case studies were chosen to explore the conditions for emergence of adaptive governance in heavily regulated and developed social-ecological systems nested within a hierarchical governmental system. We summarize resilience assessments conducted in each system to provide a synthesis and reference by the other articles in this special feature. We also present a general framework used to evaluate the interactions between society and ecosystem regimes and the governance regimes chosen to mediate those interactions. The case studies show different ways that adaptive governance may be triggered, facilitated, or constrained by ecological and/or legal processes. The resilience assessments indicate that complex interactions among the governance and ecosystem components of these systems can produce different trajectories, which include patterns of (a) development and stabilization, (b) cycles of crisis and recovery, which includes lurches in adaptation and learning, and (3) periods of innovation, novelty, and transformation. Exploration of cross scale (Panarchy) interactions among levels and sectors of government and society illustrate that they may constrain development trajectories, but may also provide stability during crisis or innovation at smaller scales; create crises, but may also facilitate recovery; and constrain system transformation, but may also provide windows of opportunity in which transformation, and the resources to accomplish it, may occur. The framework is the starting point for our exploration of how law might play a role in enhancing the capacity of social-ecological systems to adapt to climate change.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".