DOI: https://doi.org/10.1016/j.ijdrr.2022.103372
The Noah’s Ark effect
Radicalization of social meanings of disaster preparedness introduced by the estimation of a massive disaster
DOI:
https://doi.org/10.51094/jxiv.28Keywords:
Noah’s Ark Effect, Disaster Preparedness, Text Mining, Japan, TsunamiAbstract
This study proposed and demonstrated the “Noah’s Ark effect,” a concept in which massive disaster scenarios generated radicalized effects on disaster preparedness within local communities, as opposed to the cry wolf effect. The study setting was Kuroshio town in Kochi Prefecture, Japan, where the largest tsunami was expected to hit based on the Cabinet Office’s New Estimation in March 2012. The study quantitatively analyzed Japanese newspaper articles on “disaster prevention” in Kuroshio through text mining and conducted a comparison between the tsunami disaster caused by the Tohuku earthquake and the expected tsunami devastation in other areas. The results revealed that the Noah’s Ark effect is characterized by five features: (1) increasing the number of articles to the same level as that of tsunami-affected areas; (2) focusing on preventing a disaster or a hazard itself (disaster as an event) rather than preparing for issues brought about by a disaster (disaster as a process); (3) understanding changes as gradual “shifts” rather than sudden changes observed in the affected areas; (4) being typically characterized by the resident voluntary action (e.g., evacuation training) rather than their dependence on government measure; and (5) promoting specific issues and simplifying less relevant issues. The study proposed the need to consider not only the cry wolf effect, in which disaster forecasts decline disaster prevention measures, but also the Noah’s Ark effect, in which estimations of a massive disaster urge people to radically accelerate (e.g., evacuation training) and/or decrease (e.g., recovery, mourning) the degree of specific disaster prevention programs.
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Hiroaki Daimon
Genta Nakano
Kohei Takahara
Ryohei Miyamae
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