Stratifying Disaster: State Aid, Institutional Processes, and Inequality in American Communities
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
Abstract Disaster aid is an increasingly costly form of social spending and an often-overlooked way that welfare states manage new forms of risk related to climate change. In this article, I argue that disaster aid programs engender racial and socioeconomic inequalities through a process of assistance access constituted by distinct state logics, administrative burdens, and bureaucratic actors. I test this claim empirically by analyzing 5.37 million applicant records from FEMA’s Individuals and Households Program (IHP) from 2005 to 2016. Results demonstrate that key institutional features—the conditions of eligibility and sufficiency, burdens of proof, and assessments by contracted inspectors—combine in a stepwise process to funnel permanent repair resources to homeowners in whiter communities, while temporary rental aid is granted disproportionately to households in communities of color. Analyses of denial codes suggest racial disparities in appraisals of disaster damage. Among those approved for aid, more benefits accrue to those from comparatively higher income communities, and a decoupling of permanent and temporary housing aid further stratifies socioeconomic growth during recovery. Theoretically, this research advances an account of institutional processes transferable to other analyses of social programs, and it introduces climate risk as a new form of social risk against which welfare states insure citizens.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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