How historic and current wildfire experiences in an Aboriginal community influence mitigation preferences
Bibliographic record
Abstract
Peavine Métis Settlement is located in the boreal forest in Northern Alberta, Canada. The objective of this paper was to explore how different wildfire experiences in an Aboriginal community influence wildfire mitigation preferences at the residential and community levels. Residents of Peavine had varying experiences with wildfire over an extended period of time including traditional burning, firefighting employment and bystanders. Despite these different experiences, participants still implemented or supported wildfire mitigation activities, although for differing reasons depending on experience type. Participants were found to have implemented or supported wildfire mitigation activities on the settlement, including their own properties and public land. Experience type influenced why wildfire mitigation had been implemented or supported: primarily wildfire risk reduction (firefighters), primarily aesthetic benefits (bystanders) and for both aesthetic benefits and wildfire risk reduction (historic traditional burners). The extensive fire experiences of residents at Peavine Métis Settlement have provided insights into how experience influences mitigation preferences. The results show it is important to consider predominant wildfire experience types in a community before developing a wildfire mitigation program. The findings of this study may have relevance for other Aboriginal communities that have experience with wildfires.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".