JOURNAL ARTICLE

Patriotism and Resistance through Translation: Aeschylus's Persae and the Ancient Greek Past in Wartime China.

  • Published In: Classical Receptions Journal, 2025, v. 17, n. 2. P. 153 1 of 3

  • Database: Historical Abstracts with Full Text 2 of 3

  • Authored By: Yue, Mengzhen 3 of 3

Abstract

This article examines Luo Niansheng’s 1936 Chinese translation of Aeschylus’s *Persae* (The Persians) within the sociopolitical context of 1930s China, highlighting Luo’s use of the ancient Greek tragedy as a patriotic text to inspire resistance against Japanese aggression. Luo’s translation and adaptations reflect his effort to draw parallels between ancient Greek and modern Chinese experiences of invasion and national struggle, employing neologisms and culturally resonant terms such as *xila jian* (希腊奸, "Greek traitor") to convey concepts like medism while balancing dramatic propriety and empathy, notably in his neutral rendering of the Greek term *barbaros*. Supported by institutional bodies like the Committee on Editing and Translation under Hu Shi, Luo’s work was disseminated widely through public media and framed within a broader national project of cultural enlightenment. The article also situates Luo’s translation in relation to Edward Said’s concept of Orientalism, arguing that Luo’s approach, while influenced by contemporary politics and some binary views of Greeks and Persians, offers a distinct Chinese perspective that mediates ancient Greek texts for modern Chinese readers.

Additional Information

  • Source:Classical Receptions Journal. 2025/04, Vol. 17, Issue 2, p153
  • Document Type:Article
  • Subject Area:Military History and Science
  • Publication Date:2025
  • ISSN:1759-5134
  • DOI:10.1093/crj/clae019
  • Accession Number:185679115
  • Copyright Statement:Copyright of Classical Receptions Journal 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.)

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