The Stafford Act and Priorities for Reform
Bibliographic record
Abstract
During the past fifty years, federal disaster policy in the United States has been shaped by an ongoing conflict between proponents who favor federal intervention following a disaster and those who believe disaster response should be the responsibility of state and local governments and charity. This article explores the existing federal disaster policy landscape within the United States with a focus on the Stafford Act, the cultural and political forces that produced it, and how the current system is ill equipped to aid in the response and recovery from major catastrophes. The Stafford Act defines how federal disasters are declared, determines the types of assistance to be provided by the federal government, and establishes cost sharing arrangements among federal, state, and local governments. The Federal Emergency Management Agency (FEMA) carries out the provisions of the Stafford Act and distributes much of the assistance provided by the Act. With the establishment of the U.S. Department of Homeland Security, the threat of domestic terrorism, and large-scale natural disasters like Hurricane Katrina, the limits of the Stafford Act and FEMA have been shown. We look at several areas where the shortcomings of the Stafford Act have emerged and propose directions for reform.
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 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 | 0.055 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.018 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".