Exploring Wildfire Preparedness, Perceptions, and Responses in Western Canada: Insights from Valemount, British Columbia
Bibliographic record
Abstract
Abstract Climate change and decades of fire suppression are increasing the risk of wildfire in many rural and remote communities across Canada. Yet limited research has been done to better understand how these communities experience wildfire risk. For this research, conducted prior to the catastrophic wildfire season of 2023 in British Columbia, we interviewed 20 key informants living in the village of Valemount in the Robson Valley, British Columbia, about their perceptions of wildfire risk, lived experiences, and management approaches. We further explored barriers to and opportunities for future wildfire management. Our findings show that although the direct risk of wildfire impacting the village is increasing, participants mostly focused on the indirect impacts of wildfires such as power outages and the health effects of wildfire smoke. Previous experiences with these impacts, combined with a dependency on regionally managed power systems and limited transport infrastructure, were key catalysts for taking action within the village. However, several barriers, including a lack of community engagement in wildfire fighting, have impeded proactive wildfire management. Participants emphasized the need for increased support for local FireSmart initiatives and legislative changes to enable resident participation in fire suppression and to improve village preparedness. This study enhances our understanding of wildfire impacts on rural communities and outlines strategies to strengthen future wildfire management and resilience.
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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 | 0.003 |
| Science and technology studies | 0.016 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".