Disastrous Burdens: Hurricane Katrina, Federal Housing Assistance, and Well-Being
Bibliographic record
Abstract
Few existing studies of federal disaster aid examine the logics that govern assistance access. Applying the lens of administrative burdens, we identify four onerous aspects of the Federal Emergency Management Agency’s (FEMA) housing aid program—regulations regarding application unit, documentation, and damage sufficiency, and long processing times—that prompt assistance delay or denial for in-need households. Our empirical strategy pairs administrative records from FEMA on denied applications (<i>N</i> = 206,157) after Hurricanes Katrina and Rita with survey (<i>N</i> = 354) and in-depth interview data (<i>N</i> = 106) from a longitudinal study of low-income survivors of Katrina. Results show that applications from poor, communities of color were disproportionately denied or delayed due to burdensome program requirements and their implementation. Interviews and survey evidence elucidate the compliance costs and suggest a toll on post-disaster well-being.
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.
How this classification was reachedexpand
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".