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.
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.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it