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Record W2123219370 · doi:10.2202/1547-7355.1538

The Stafford Act and Priorities for Reform

2009· article· en· W2123219370 on OpenAlexfundno aff
Mitchell Moss, Charles Schellhamer, David A. Berman

Bibliographic record

VenueJournal of Homeland Security and Emergency Management · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersFederal Emergency Management AgencyYork UniversityU.S. Department of Homeland Security
KeywordsHomeland securityPublic administrationEmergency managementState (computer science)Political scienceGovernment (linguistics)TerrorismNatural disasterPoliticsAgency (philosophy)Reform ActLawSociology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.294
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations64
Published2009
Admission routes1
Has abstractyes

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