Forest as hazard, forest as victim: Community perspectives and disaster mitigation in the aftermath of Kelowna's 2003 wildfires
Bibliographic record
Abstract
Situated within the political ecology of hazard, this article is an extended case study of the devastating 2003 wildfires in and around Kelowna, British Columbia (also known as the Okanagan Mountain Park Fire). This article reveals how compliance (or lack thereof) with fire mitigation strategies recommended by provincial, regional, and municipal agencies is complicated by differing social constructions of what constitutes ecologically sustainable forest management and community safety. Three perspectives emerge regarding the urban forests: “nature as hazard”—a volatile force to be controlled; “nature as instrumentally valuable”—a contribution to the character of one's surroundings and subsequent sense of place; and “nature as intrinsically valuable”—a distinct entity to be preserved and protected for its own sake. The article also examines how experiences of disaster influence community perceptions and result in a greater willingness to engage in fire mitigation strategies due to perceptions of heightened vulnerability. Forestry and fire mitigation agencies need to determine multiple courses of action among the varied and valid range of residents’ nature perspectives. The role of human agency in disaster mitigation must be examined, particularly as the risk of fire at the wildland‐urban interface continues to be exacerbated by encroaching human settlements and climate change.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".