Bibliographic record
Abstract
Climate change and population settlement patterns are altering the severity and spatial dimensions of flooding. Despite associational evidence linking flood exposure to population health in the United States, few studies have used counterfactual strategies to address confounding or examined how sociospatial determinations of risk, such as floodplain delineation, affect well-being. Using the case of Hurricane Harvey, I leverage novel, repeated cross-sectional health survey data from Houston immediately predisaster (N = 2,540) and six to nine months postdisaster (N = 2,798), linked to local flood inundation and floodplain data. Difference-in-differences models show that the probability of psychological distress and fair/poor health increased significantly in the flooded treatment group, with mixed evidence on unhealthy mental health days and no change in unhealthy physical health days. Triple-difference estimators further reveal buffered mental health adversity for those in flooded areas with high floodplain areal coverage relative to little or no floodplains. Descriptive analyses of mechanisms suggest that floodplain coverage did not differentiate individual-level disaster exposure but increased the likelihood of disaster preparedness and evacuation. This article offers insights into the climate-health nexus empirically by using a causal framework to improve credibility and conceptually by demonstrating how an underexamined dimension of vulnerability-sociospatial risk determinations-can stratify population health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".