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Forest as hazard, forest as victim: Community perspectives and disaster mitigation in the aftermath of Kelowna's 2003 wildfires

2012· article· en· W1498391401 on OpenAlexaffvenueabout
Magdalene Goemans, Patricia Ballamingie

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsCarleton University
FundersUniversity of Georgia
KeywordsVulnerability (computing)Human settlementAgency (philosophy)Wildland–urban interfaceHazardEnvironmental planningEnvironmental resource managementGeographyEmergency managementSocial vulnerabilityPolitical scienceEcologyPsychological resilienceSociologyEnvironmental sciencePsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.566

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0280.018
Scholarly communication0.0090.004
Open science0.0010.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.004
GPT teacher head0.187
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2012
Admission routes3
Has abstractyes

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