How social and ecological characteristics shape transaction costs in polycentric wildfire governance: insights from the Sequoia-Kings Canyon Ecosystem, California, USA
Bibliographic record
Abstract
Many contemporary social and ecological challenges in forested ecosystems (climate change, invasive species, wildland-urban interface development, and wildfires) span multiple jurisdictions and are characterized by complex patterns of social and ecological interdependencies. Increasing evidence suggests that interdependent risk can best be addressed by working across boundaries (jurisdictional, scalar, and expertise) by sharing information and cooperating in management activities. Polycentric governance has emerged as a framework to understand how multiple and overlapping centers of decision-making authority establish and maintain governance connectivity to solve collective action problems and interdependent risks. Previous studies have examined the collaborative and interorganizational process of polycentric landscape governance, yet most studies rely on qualitative case study data or descriptively employ social network analysis. Understanding the values, beliefs, and motivations of actors (land managers, landowners, researchers, policymakers, and non-governmental organizations) for cooperating is important for improving polycentric governance design, implementation, and operation. How the context and characteristics of social-ecological systems shape polycentric governance remains largely unexplored. On the basis of research in the Sequoia-Kings Canyon Protected Area-Centered Ecosystem, we address this gap by utilizing exponential random graph modeling to analyze the social and ecological drivers of polycentric wildfire governance. This research highlights that even in situations of high stakes (increasing occurrences of high-severity wildfires that escape suppression) actors will collaborate only if the gains from collaboration outweigh the costs. If jurisdictions or other organizations are thought to have low operational capacity or lack useful information, even with a high probability of large wildfire, the actor-to-actor connections are less likely for effective polycentric governance. Our results highlight previously undiscussed mechanisms of network formation in wildfire hazard governance, and we discuss the broader applicability for forest landscape challenges and for polycentric governance design and assessment in other social-ecological contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".