Publication Cover
Perspectives
Studies in Translation Theory and Practice
Volume 29, 2021 - Issue 3
3,084
Views
12
CrossRef citations to date
0
Altmetric
Articles

Reshaping China’s image: a corpus-based analysis of the English translation of Chinese political discourse

& ORCID Icon
Pages 354-370 | Received 10 Aug 2019, Accepted 03 Feb 2020, Published online: 20 Feb 2020
 

ABSTRACT

Drawing on a combined framework of Appraisal System and Ideological Square Model, this paper conducts a corpus-based investigation of the ways in which the image of China is (re)shaped in the English translation of Chinese political discourse in terms of appraisal epithets. The results show that (1) shifts regularly occur in the English translation of the appraisal epithets in Chinese political discourse, though an equivalent translation strategy is a canonical option for the translators of Chinese political discourse; (2) translation patterns of the appraisal epithets vary within the three sub-categories of Appraisal System, with shifts found mostly in the translation of the negative appraisal epithets under ‘engagement’ and ‘graduation’ subcategories; (3) discursively, China is more negatively represented in the translated than in the source Chinese texts. A two-layered Ideological Square Model is proposed to account for the research findings in terms of ideological factors in the translation of Chinese political discourse.

Acknowledgements

We would like to thank Prof. Kaibao Hu at Shanghai International Studies University, Prof. Yifan Zhu and Dr. Kyung Hye Kim at Shanghai Jiao Tong University for their helpful suggestions. We also owe great thanks to the anonymous reviewers for their constructive comments.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes on contributors

Tao Li is Associate Professor in translation studies at the Centre for Corpus Research, Shanghai Ocean University. He holds a PhD in Translation Studies from Shanghai Jiao Tong University. His research interests cover corpus-based translation studies, systemic functional linguistic approach to the analysis of translation and interpreting, discourse analysis. He has published articles in journals such as Discourse & Society (2020), Discourse, Context & Media (2018), Modern Foreign Languages (2015, in Chinese).

Feng Pan is Associate Professor of interpreting and translation studies at the Department of Translation and Interpreting, Huazhong University of Science and Technology, China. He holds a PhD in Translation and Intercultural Studies from Shanghai Jiao Tong University. His academic interests include corpus-based translation studies, discourse analysis, and conference interpreting. His work has appeared in The Journal of Specialised Translation (2020, forthcoming), Across Languages and Cultures (2017), Babel (2016), Foreign Language Teaching and Research (2015, in Chinese) among others.

Notes

2 The agreement coefficient of Gwet’s AC1 is adopted here, whereby 1 signifies absolute agreement and 0.67 marks a cutting line between agreement and disagreement. So the result of 0.92 indicates a very high agreement between the two raters.

Additional information

Funding

This article is a part of the project funded by Shanghai Planning Office of Philosophy and Social Science ‘The discursive pattern of national image in the English translation of Chinese political discourse’ (2017BYY009).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 178.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.