Community Forests advance local wildfire governance and proactive management in British Columbia, Canada
Bibliographic record
Abstract
As wildfires are increasingly causing negative impacts to communities and their livelihoods, many communities are demanding more proactive and locally driven approaches to address wildfire risk. This marks a shift away from centralized governance models where decision-making is concentrated in government agencies that prioritize reactive wildfire suppression. In British Columbia (BC), Canada, Community Forests—a long-term, area-based tenure granted to Indigenous and/or local communities—are emerging as local leaders facilitating proactive wildfire management. To explore the factors that are enabling local governance approaches to managing wildfire risk, we conducted semi-structured interviews with 26 Community Forest managers across BC. Managers highlighted financial and social capacity, especially trust and relationships with both community members and government agencies, as crucial factors influencing their ability to undertake proactive management. These factors enable Community Forests to address wildfire risk not only within their own tenure area, but also at household, community, and landscape scales, while balancing diverse community values, objectives for forest management, and legal and policy obligations. Despite ongoing challenges, Community Forests emphasized the importance of scaling up their efforts to address wildfire risk and are a critical form of local wildfire governance that can help advance proactive wildfire management across BC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".