To burn or not to burn: governance of wildfires in Australia
Bibliographic record
Abstract
Globally, wildfires are increasing in extent, frequency, and severity. Although global climate change is a major driver and large-scale governance interventions are essential, focusing on governance at smaller scales is of great importance for fostering resilience to wildfires. Inherent tensions in managing wildfire risk are evident at such scales, as objectives and mandates may conflict, and trade-offs and impacts vary across ecosystems and communities. Our study feeds into debates about how to manage wildfire risk to life and property in a way that does not undermine biodiversity and amenity values in social-ecological systems. Here, we describe a case study where features of adaptive governance emerged organically from a dedicated planning process for wildfire governance in Australia. We found that a governance process that is context specific, allows for dialogue about risk, benefits, and trade-offs, and allows for responsibility and risk to be distributed amongst many different actors, can provide the conditions needed to break down rigidity traps that constrain adaptation. The process enabled actors to question whether the default risk management option (in this case, prescribed burning) is aligned with place-based risks and values so they could make an informed choice, built from their participation in the governance process. Ultimately, the community supported a move away from prescribed burning in favor of other wildfire risk management strategies. We found that the emergent governance system has many features of adaptive governance, even though higher level governance has remained resistant to change. Our study offers positive insights for other governments around the world interested in pursuing alternative strategies to confronting wildfire risk.
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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 | 0.002 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".