JOURNAL ARTICLE

Canonizing Arthur Waley, rewriting Murasaki Shikibu: The Japanese back-translations of Waley's The Tale of Genji.

  • Published In: Target: International Journal on Translation Studies, 2025, v. 37, n. 3. P. 360 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Chan, Leo Tak-hung; Ni, Jindan 3 of 3

Abstract

This article examines the two Japanese back-translations of Arthur Waley's English rendition (1925–1933) of Murasaki Shikibu's 源氏物語   Genji monogatari 'The tale of Genji' to underscore the complexities of back-translation as process and product. The back-translations by Samata Hideki (2008–2009), and Mariya Marie and Moriyama Megumi (2017–2019) are clearly attempts to reinvigorate the millennium-old Japanese tale and renew interest among the domestic readership, but they also serve to canonize Waley's version. It is important that these two back-translations be read against a long history of successful translations of the novel — both intralingual and interlingual — to see the peculiarities of the new textual interventions. Unlike what usually happens with translations in general, the back-translators engage with two source texts (by Shikibu and by Waley) instead of one. Through close textual analysis, this study aims to demonstrate how back-translations can be an ideal site for exploring issues related to rewriting, canonization, retranslation, and textual authority in historical and cultural contexts. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Target: International Journal on Translation Studies. 2025/09, Vol. 37, Issue 3, p360
  • Document Type:Article
  • Subject Area:Biography
  • Publication Date:2025
  • ISSN:0924-1884
  • DOI:10.1075/target.23158.cha
  • Accession Number:187643349
  • Copyright Statement:Copyright of Target: International Journal on Translation Studies is the property of John Benjamins Publishing Co. 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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