Incentives and Barriers to Homeowners’ Uptake of FireSmart® Canada’s Recommended Wildfire Mitigation Activities in the City of Fort McMurray, Alberta
Bibliographic record
Abstract
This paper presents the results of a survey that was undertaken to examine homeowners’ FireSmart mitigation practices and investigate existing incentives and barriers to uptake of FireSmart Canada’s recommended wildfire mitigation activities in the Urban Service Area of Fort McMurray Alberta. Single-family residential property owners, the large majority of whom were affected by the Horse River wildfire, were invited to participate in an online survey. A total of 496 surveys were completed, with a response rate of 38%. We found that most of the participants generally perceive a low to moderate wildfire risk to their properties: they felt there was a low chance of a catastrophic fire happening soon and/or ‘enough’ had already been done to reduce the immediate risk. Although about half of the participants searched for information about FireSmart, having information or knowledge of FireSmart did not translate into substantial adoption of recommended mitigation actions. Survey participants generally preferred and implemented more of the low-cost, low effort mitigation measures such as cutting grasses and cleaning debris, likely for reasons other than wildfire risk reduction. With regard to structural measures, we found asphalt shingles and vinyl siding were present on the majority of homes; although this was not a choice but was provided by the builder or on the home when it was purchased. Very few respondents were willing to replace their siding or roof––the cost was the single biggest factor. In addition, we identified several other factors as negatively influencing homeowners’ mitigation actions, including the tendency to shift responsibility to the municipal government and social pressure such as neighbors not being as proactive in completing FireSmart mitigation measures. Recommendations that may help promote positive wildfire mitigation behaviors are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".