Learning from Wildfire: Understanding the Social Dimensions of Community Response
Bibliographic record
Abstract
Wildfires are becoming more frequent and intense in Canada’s boreal forest region. Research shows that the social dimensions of communities’ experiences of wildfires and other disasters are influenced by identity factors such as gender, culture, age, and others. However, few studies have examined how these identity factors intersect to create layered, context-specific experiences of disaster. Rather, most research on the wildfire threat in Canada’s boreal forest focuses on physical and technical infrastructure, largely ignoring the social dimensions that affect communities’ capacity to prepare for and respond to disaster. Additionally, within the four-stage disaster management cycle (mitigation, preparedness, response, recovery), there is a missed opportunity for post-disaster learning (PDL): the direct, experiential learning from a wildfire event that can be translated into enhanced, socially sensitive, wildfire disaster management plans. A focus on identity factors, especially how they intersect to shape experiences of wildfire, and social strategies tailored to local context, can inform the development of a community-based framework for PDL. Focusing on two rural municipalities in Northern Saskatchewan who experienced wildfires in 2015, this research has identified key social factors and social strategies that shaped communities’ experiences of a wildfire disaster. Data from interviews and workshops with local residents, together with results from analysis of community-based wildfire and disaster management guides, provided the basis for the co-development of a socially-focused framework for community-based PDL. This framework can be used by rural Canadian communities to learn from their lived experiences with wildfire and other disasters to enhance the ability both to mitigate social impacts of wildfires and to guide future community wildfire management plans. This research also contributes to scholarly methodological knowledge on the use of feminist standpoint theory and intersectionality as means to better understand and address diverse disaster experiences.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.023 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| 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".