Governing wildfires: toward a systematic analytical framework
Bibliographic record
Abstract
Despite recent research, a systematic approach to understanding wildfire governance is lacking. This article addresses this deficit by systematically reviewing governance theories and concepts applied so far in the academic literature on wildfires as a step toward achieving their more effective and holistic management. We engage our findings with the wider governance literature to unlock new thinking on wildfires as a process and outcome. This comparative approach enables us to propose a novel framework for analyzing wildfire governance based on four pillars: (1) actor participation in decision-making and decision taking; (2) actor collaboration and coproduction across and within levels, scales, and networks; (3) path dependencies and local place-based dynamics of wildfire incidence and comprehension; and (4) actor adaptation to and anticipation of wildfire risk to fashion effective institutions that address the global wildfire challenge. We show how this framework can help specify a suite of bespoke analytical and policy practitioner approaches to facilitate preemptive and restorative wildfire strategies via new networks between communities, states, and wider society, thus providing the basis for more equitable and sustainable governance of wildfire risks and impacts.
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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 | 0.040 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.014 | 0.020 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".