Built by Fire: Wildfire Management and Policy in Canada
Bibliographic record
Abstract
abstract: Wildfire is an inescapable feature of Canadian landscapes, burning an average of over two million hectares annually and causing significant repercussions for communities, infrastructure, and resources. Because fire is managed provincially, each jurisdiction has developed a distinctive approach to preparing for, responding to, and recovering from fire on its landscapes. Using a comparative study between seven provinces and four national agencies, this dissertation examines differences in institutional design and policy with respect to the knowledge management systems required to respond to wildfire: How do policies and procedures vary between jurisdictions, how do they affect the practices of each fire management agency, and how can they be improved through a critical analysis of the knowledge management systems in use? And, what is the role of and limits on expertise within these fire management institutions that manage high-risk, highly uncertain socio- environmental challenges?\n\nI begin by introducing the 2016 Fort McMurray/Horse River fire as a lens for exploring these questions. I then use the past one hundred years of fire history in Canada to illustrate the continual presence of fire, its human and social dimensions, and the evolution of differing fire management regimes. Drawing on extended ethnographic observation and interviewing of fire managers across Canada, I examine the varied provincial systems of response through following an active fire day in Alberta. I analyze the decision support and geospatial information systems used to guide fire agency decision-making, as well as the factors that limit their effectiveness in both response and hazard reduction modes. I begin Part Two with a discussion of mutual aid arrangements between the provinces, and critically examine the core strategy – interagency fungibility – used to allow this exchange. I analyze forecasting and predictive models used in firefighting, with an emphasis on comparing advantages and disadvantages of attempts at predicting future firefighter capacity requirements. I review organizational learning approaches, considering both fire research strategies and after action reviews. Finally, I consider the implication of changes in climates, politics, and public behaviours and their impacts on fire management.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 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".