Representation of the Fukushima nuclear disaster in media opinion pieces.

  • Published In: Text & Talk, 2026, v. 46, n. 1. P. 101 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Reid, Samuel 3 of 3

Abstract

The Fukushima nuclear power plant meltdown is the second worst accident in the history of nuclear power. As nuclear power is a controversial policy option, interpreting the significance of the disaster is important in the struggle over the legitimacy of nuclear power. This article compares the representation of Fukushima between a corpus of five pro-nuclear and five anti-nuclear media opinion pieces published in English-language online newspapers. It uses systemic functional linguistics to examine the verbal process types and clausal positioning of Fukushima, focusing on themes in the data and how these representations construct competing arguments about the disaster. In pro-nuclear articles Fukushima has more frequent Relational and Mental process types and has more foregrounded clausal positioning, which reflects the need to emphasise alternative interpretations to the narrative of Fukushima as an unacceptable disaster. In anti-nuclear articles there are more frequent Material processes and backgrounded clausal positioning, which reflects a focus on the physical damage of the disaster and an assumption that Fukushima is evidence of the fallibility of nuclear power. Discursive differences over three specific aspects are identified – the lesson to be taken from Fukushima, the assessment of damage caused, and the performance of the nuclear plant. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Text & Talk. 2026/01, Vol. 46, Issue 1, p101
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2026
  • ISSN:1860-7330
  • DOI:10.1515/text-2024-0022
  • Accession Number:190801153
  • Copyright Statement:Copyright of Text & Talk is the property of De Gruyter and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.