Community Engagement With Proactive Wildfire Management in British Columbia, Canada: Perceptions, Preferences, and Barriers to Action
Bibliographic record
Abstract
Wildfires in the wildland-urban interface (WUI) are increasingly threatening lives and livelihoods. These growing impacts have prompted a paradigm shift toward proactive wildfire management that prioritizes prevention and preparedness instead of response. Despite this shift, many communities remain unprepared for wildfires in the WUI due to diverse individual and social-political factors influencing engagement with proactive management approaches. The catastrophic fire seasons of 2017, 2018, and 2021 in British Columbia (BC), Canada, highlighted just how vulnerable communities continue to be and the urgent need to understand the factors limiting engagement to future resilience to wildfire. Our study, conducted prior to the catastrophic fire season in 2017, surveyed 77 community leaders across BC to better understand the factors driving engagement, including risk perception, preferences and support for approaches, and key barriers limiting progress. We demonstrate that wildfire risk is an urgent issue facing communities across BC, but a range of factors drive variable community engagement with proactive wildfire management. First Nations and smaller (≤5,000 residents) communities were less likely to have developed a community wildfire plan, even though First Nations were significantly more concerned than municipalities/regional districts about certain values (such as drinking water and biodiversity) that were at risk from wildfire. In general, proactive approaches that were considered effective were also the most supported. The most highly supported approaches included enforcement of regulations and education, both of which are considered provincial responsibility in BC and are unlikely to alter community values in the WUI. In contrast, approaches involving prescribed burning of the understory had the highest levels of opposition. Despite variability in these individual factors, social-political barriers related to financial and social (time and expertise) capacity primarily limited engagement with proactive wildfire management, including provincial and federal funding programs. However, these barriers are not equally felt across community groups; First Nations identified social capacity (such as expertise on government-sponsored approaches and awareness of funding programs) as significantly more limiting than municipalities/regional districts. Our study illustrates the limitations of implementing a “shared responsibility” of proactive wildfire management in the WUI in BC without targeted supports to address unequal capacity barriers.
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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 | 0.015 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".