A survey of Chinese Interpreting Studies: Who influences who … and why?

Intercultural Studies Group, Universitat Rovira i Virgili, Tarragona, Spain
Department of Statistics, Stanford University, Stanford, California, United States
DOI
10.7287/peerj.preprints.941v2
Subject Areas
Data Mining and Machine Learning, Data Science, Digital Libraries, Social Computing
Keywords
Chinese Interpreting Studies, academic influence, social network analysis, research topic selection
Copyright
© 2015 Xu et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Xu Z, Pekelis LB. 2015. A survey of Chinese Interpreting Studies: Who influences who … and why? PeerJ PrePrints 3:e941v2

Abstract

This paper describes how scholars in Chinese Interpreting Studies (CIS) interact with each other and form discrete circles of influence. It also discusses what it means to be an influential scholar in the community and the relationship between an author’s choice of research topic and his academic influence. The study examines an all-but-exhaustive collection of 59,303 citations from 1,289 MA theses, 32 doctoral dissertations and 2,909 research papers, combining traditional citation analysis with the newer Social Network Analysis to paint a panorama of CIS. It concludes that the community cannot be broadly divided into Liberal Arts and Empirical Science camps; rather, it comprises several distinct communities with various defining features. The analysis also reveals that the top Western influencers have an array of academic backgrounds and research interests across many different disciplines, whereas their Chinese counterparts are predominantly focused on Interpreting Studies. Last but not least, there is found to be a positive correlation between choosing non-mainstream research topics and having a high level of academic influence in the community.

Author Comment

This is version 2 of our submission to PeerJ Computer Science for review. Substantial detail was provided for the data visualization procedures.