Wildfire governance in a changing world: Insights for policy learning and policy transfer
Bibliographic record
Abstract
Abstract Societies must learn to live with, and adapt to wildfire risk. Here we examine wildfire governance and policy in British Columbia (BC), Canada over the last two decades, to examine how policy lessons are drawn from wildfire events. We focus on independent reviews and their recommendations provided, lessons learned from abroad, and whether policy and governance has changed (or not). Jurisdictional and intercultural issues in BC's wildfire response are outlined in this paper, and opportunities for innovative solutions are examined. We then present a case study of the Tsilhqot'in Fire Management program to demonstrate how Indigenous Fire Management is being revitalized as a proactive solution to wildfire. Our intent is to reveal why policy learning and transfer from Indigenous peoples is increasing in this context, and we identify how this is occurring. Barriers to implementation are outlined, and implications for wildfire governance in BC and globally are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".