Assets and Ashes: Wildfire management and the politics of climate change
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| 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".