JOURNAL ARTICLE

Destigmatizing illegitimacy: the discursive reconfiguration of non-marital children in Heisei-Era Japan.

  • Published In: International Journal of Law, Policy & the Family, 2025, v. 39, n. 1. P. 1 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Wu, Jiangcheng; Li, Hanyi 3 of 3

Abstract

This article examines the destigmatization of non-marital children in Japan during the Heisei era (1989–2019) through a critical discourse analysis of media, legal reforms, and social narratives. Historically marginalized by discriminatory legal provisions and stigmatizing media portrayals, non-marital children faced significant social and legal disadvantages, particularly regarding inheritance rights and family registration. Over time, key social actors—including public authorities, intellectuals, media, ordinary citizens, and private organizations—contributed to shifting discourse from legal distinctions toward human rights and social inclusion, culminating in landmark legal reforms such as the 2013 Supreme Court ruling that declared inheritance inequality unconstitutional. This transformation occurred alongside broader social changes, including the decline of the traditional family model and increasing acceptance of diverse family structures, reflecting a complex interplay between legal, social, and cultural factors in reshaping stigma in contemporary Japanese society.

Additional Information

  • Source:International Journal of Law, Policy & the Family. 2025/01, Vol. 39, Issue 1, p1
  • Document Type:Article
  • Subject Area:Religion and Philosophy
  • Publication Date:2025
  • ISSN:1360-9939
  • DOI:10.1093/lawfam/ebaf027
  • Accession Number:191632989
  • Copyright Statement:Copyright of International Journal of Law, Policy & the Family is the property of Oxford University Press / USA 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.)

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