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Record W4312531296 · doi:10.1177/23780231221135971

Climate-Related Disasters and Children’s Health: Evidence from Hurricane Harvey

2022· article· en· W4312531296 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.

Bibliographic record

VenueSocius Sociological Research for a Dynamic World · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFlooding (psychology)Extreme weatherClimate changeImmigrationDemographyGeographyPsychologyEnvironmental healthGerontologyMedicineSociology

Abstract

fetched live from OpenAlex

Children have been theorized as vulnerable to the health consequences of climate change, but data limitations have hampered prior studies of climate-related disasters in the United States. In this article, the author exploits the interruption of a health survey in Houston by Hurricane Harvey, linked to local flooding data ( n = 1,123, ages 5–17 years). Multivariable models on a matched sample show that Harvey led to worse parent-reported health among children six to nine months later, particularly in flooded communities. Further evidence suggests that household life disruption and home damage were key mechanisms and that severe exposure correlated with larger health declines among immigrants, including Hispanic and Asian or other-race children and those younger than 10 years. Integrating these findings with life-course theory and climate science, the author argues that through disasters, climate change should be conceptualized as a risk factor for heath and intragenerational disparities within cohorts and for intergenerational inequalities as newer cohorts experience more extreme weather.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.193
GPT teacher head0.450
Teacher spread0.257 · 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