JOURNAL ARTICLE

A Comparison of Styles across Three Versions of Cranford in Chinese: 1927, 1937 and 1985.

  • Published In: World & Word / Świat & Słowo, 2023, v. 41, n. 2. P. 133 1 of 3

  • Database: Central & Eastern European Academic Source 2 of 3

  • Authored By: Lisu Wang 3 of 3

Abstract

With the differences between westernized vernacular Chinese and mature modern Chinese, the three versions of Cranford show the development of the modern Chinese language: Woo Kwang Kien's Cranf in 1927, Zhu Manhua's The Forbidden City for Women in 1937, and Xu Xin and Gu Mingdong's 1985 version published with the original title Cranford. As viewed from the overall result, Woo's and Zhu's translations are rich both in classical Chinese elegance and western style, embracing deep personal emotions, while Xu's shows the well-developed quality of the Chinese language. As to the representation of the original linguistic features, Woo's and Zhu's translations partly present the original text, as the early modem Chinese that they employ is full of expressions with western characteristics. On the other hand, Xu's version with its authentic modern Chinese and flexible approaches, represents Gaskell's linguistic features in a more perfect way. This paper discusses the styles and effects in these versions from the following aspects: (1) beauty in classical Chinese; (2) the charm of appellation; (3) the translation of idiom; (4) rhotic accent and dialect; and (5) excessive westernization and modern Chinese. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:World & Word / Świat & Słowo. 2023/07, Vol. 41, Issue 2, p133
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2023
  • ISSN:1731-3317
  • DOI:10.53052/17313317.2023.32
  • Accession Number:175403078
  • Copyright Statement:Copyright of World & Word / Świat & Słowo is the property of University of Bielsko-Biala 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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