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Record W4412771564 · doi:10.1142/s2010007825500125

NATURAL DISASTERS AND INDIVIDUAL ECONOMIC PERFORMANCE: A CASE STUDY FROM THE SLAVE LAKE WILDFIRE

2025· article· en· W4412771564 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueClimate Change Economics · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of OttawaUniversité du Québec à Rimouski
Fundersnot available
KeywordsNatural disasterAgricultureGeographyFirefightingEconomicsAgricultural economicsEconomic impact analysisEnvironmental scienceDemographic economicsSocioeconomicsNatural resource economicsMeteorologyCartography

Abstract

fetched live from OpenAlex

In May 2011, the municipality of Slave Lake, Alberta, was hit by a devastating wildfire; the second costliest natural disaster in Canada at the time. Residents of Slave Lake were forced to evacuate for at least a month. This case study uses longitudinal income tax data from 2004 to 2018 to estimate the short, medium, and long-term individual economic effects of this wildfire. Estimates suggest an average drop in total income of 10.5% relative to a counter-factual scenario with no wildfire over the 7 years following the wildfire, mainly driven by a decrease in employment income. The percentage of total income lost is similar for males and females. The largest effects are found for workers in the agriculture and forestry sectors. Back-of-the- envelope calculations suggest an aggregate loss in employment income of $150 million in the 7 years following the disaster, equivalent to over 13% of direct economic losses due to property damage, firefighting, and contemporaneous business closure.

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 score0.054
Threshold uncertainty score0.970

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 science0.0000.001
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 head0.020
GPT teacher head0.229
Teacher spread0.209 · 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