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Record W4410825640 · doi:10.1016/j.esg.2025.100254

Assets and Ashes: Wildfire management and the politics of climate change

2025· article· en· W4410825640 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEarth System Governance · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité LavalUniversity of Calgary
FundersPierre Elliott Trudeau Foundation
KeywordsClimate changePoliticsEnvironmental resource managementEnvironmental planningPolitical scienceEnvironmental scienceGeographyBusinessGeologyOceanographyLaw

Abstract

fetched live from OpenAlex

This Perspective examines the interplay between natural and political systems. Drawing on first-hand experience and recent studies, it employs various graphic novel techniques to illustrate feedback loops that connect the expansion of extractive industries into the urban-wildland interface, the incidence of human-induced wildfires, the use of prohibition policies in wildfire management to protect specific assets, the accumulation of combustible materials, the emission of greenhouse gases from wildfires, and the acceleration of climate change. However, it challenges the notion of endless self-reinforcing vicious cycles by demonstrating how shifts in asset valuation can catalyze a new politics of climate change. Forestry might become a potential vanguard example of an industry shifting its self-perceptions and political alignments in the face of more climate change induced wildfires. This transdisciplinary artistic Perspective builds on and speaks to research in the fields of forest management, climate politics, wildfire sociology, and human ecology.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.189
Teacher spread0.185 · 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