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Record W2164316805 · doi:10.4296/cwrj3002145

Public Participation in the Emergency Response Phase of Flooding: A Case Study of the Red River Basin

2005· article· en· W2164316805 on OpenAlexfundvenueaboutno aff
Jacqueline K Wachira, A. John Sinclair

Bibliographic record

VenueCanadian Water Resources Journal / Revue canadienne des ressources hydriques · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersGovernment of Canada
KeywordsFlood mythContext (archaeology)Flooding (psychology)Public relationsEmergency managementEnvironmental planningEmergency responseQualitative researchPublic opinionPhase (matter)Political scienceBusinessGeographySociologyPsychologyMedical emergencyMedicinePoliticsSocial scienceLawArchaeology

Abstract

fetched live from OpenAlex

Emergency flood response is a controversial phase in flood management mostly because there is virtually no public input into important decisions such as evacuation orders. Little attention has been paid to the potential for involving the public more in decision-making in this phase of flood management. The purpose of the study was to investigate whether more public involvement in the emergency response phase would create greater support for government action, minimize uncertainty and dissatisfaction, and improve overall flood management. The specific objectives were to: l) identify and describe key publics, government agencies, and civic organizations involved in emergency flood response; 2) determine the understanding that the public had of their role in emergency flood response; 3) identify and describe interactions among key participants during the emergency flood response phase; 4) evaluate public involvement practices in the emergency response phase; and 5) develop recommendations to improve public involvement in the emergency response phase of a flood. A case study approach involving two communities from Canada and the United States in the Red River Basin was used to accomplish the objectives of the study. One community was the village of Rosenort in Manitoba, and the other was the city of Drayton in North Dakota. Data collection methods included document review and semi- structured qualitative interviews...

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.063
GPT teacher head0.306
Teacher spread0.243 · 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.

Study designQualitative
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

Citations4
Published2005
Admission routes3
Has abstractyes

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