Burning Questions: Community Engagement and Wildfire Management in a Changing Climate. A case study of Kamloops, British Columbia, Canada
Bibliographic record
Abstract
As Climate Change increases the frequency and severity of wildfires in British Columbia, communities at the Wildland Urban Interface face escalating risks. This thesis explores the challenges and opportunities of community engagement in wildfire management in the city of Kamloops, one of the most fire-prone urban areas in British Columbia. Employing a single-case study design, the research is based on 26 semi-structured interviews with both community members and institutional actors. The thesis investigated three central dimensions: perceptions of responsibility in wildfire management, the state and potential of community engagement, and the role of climate change risk perception in shaping engagement efforts. The findings show that while community members and institutional actors recognise the importance of shared responsibility, significant mismatches persist in their expectations, particularly regarding evacuation and property protection. Trust in institutions, particularly Kamloops Fire Rescue, is generally high, yet concerns about institutional capacity and responsiveness remain. Persistent barriers include limited communication, seasonal disengagement, financial and time constraints, and competing local priorities. Nevertheless, opportunities exist to strengthen community engagement through diversified communication strategies, increased funding, educational initiatives, and the use of community champions. Climate change is broadly acknowledged as a driving factor behind worsening wildfire seasons, but its influence on individual engagement remains limited compared to more immediate, visible wildfire threats. The thesis concludes that inclusive, context-sensitive engagement approaches are critical for building wildfire resilience in an era of accelerating climate risk.
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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 | 0.028 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".