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Intercrisis learning in disaster response network

Experience of Korea from Mers and Covid-19

Datos Bibliográficos

ID7262069
AutoresRan Kim (0000-0003-1828-2301, Seoul National University), Hyun-Jae Shin (0000-0001-9452-7687, Seoul National University), Phil Kim (0000-0001-9911-9711, Center of Intelligent Society and Poilicy, Seoul National University, Gwanak-gu, Korea)
Año2023
Volumen31
Número1
Páginas40-62
Fecha de publicación2023-01-02
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaAsian Journal of Political Science (JOURNAL)
Identificadores de la revistaISSN: 0218-5377 • E-ISSN: 1750-7812
EditorialTaylor & Francis (PUBLISHER • GB)
DOI10.1080/02185377.2022.2157295
OpenAlexW4318476509
IdiomaEN
Citas recibidas3
Referencias citadas40

This paper critically reviews whether the hierarchical system or intercrisis learning can be sufficient to understand Korea's COVID-19 responses. Our case study suggests that a Korean response system is a hybrid form that uses a hierarchical structure together with a network approach. To unveil theoretical models of how learning may occur and evolve during a crisis, we employ a policy learning model combining the network perspective and the four Cs model (cognition, communication, coordination, and control). We analyse the change in government manuals, response policies, and agenda streams observed in government documents. This analysis reveals far more complex interactions among actors and policies, both flexible and rigid at different phases of COVID-19. On top of policy learning, we conclude that it is necessary to rediscover the power of citizen voluntary responses and collaboration among actors of the response network through value change

Cognition · Control (management · Coronavirus disease 2019 (COVID-19 · Disaster response · Emergency management · Government (linguistics · Knowledge management · Machine learning · Perspective (graphical · Policy learning · Political science · Public relations · Computer Science · Disaster Management and Resilience · Disaster Response and Management · Flood Risk Assessment and Management · Law · Medicine · Psychology · Artificial Intelligence

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Obras citantes distintas3
Citas por año1,5
Intervalo de citas2024 - 2024 (1)
Velocidad de citaciónrecent
Altamente citadoNo
Tipos de citaNeutras: 3
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