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

Abstract

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 EmpiricalScience 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.

Peer Review #2 of "Chinese Interpreting Studies: a data-driven analysis of a dynamic field of enquiry (v0.1)"

“…Examining these three strands individually is necessary if we are to fully understand how each contributes to advancing the field as a whole. Building on earlier studies by the present authors (Xu 2014(Xu , 2015aXu & Pekelis, 2015), which provided an overview of the field from different perspectives, this study uses some of the most sophisticated data-mining techniques currently available to answer the aforementioned complex questions, none of which can be adequately addressed by simple descriptive statistics.…”

Chinese Interpreting Studies: a data-driven analysis of a dynamic field of enquiry

“…Examining these three strands individually is necessary if we are to fully understand how each contributes to advancing the field as a whole. Building on earlier studies by the present authors ( Xu, 2014 ; Xu, 2015a ; Xu, 2015b ; Xu & Pekelis, 2015 ), which provided an overview of the field from different perspectives, this study uses some of the most sophisticated data-mining techniques currently available to answer the aforementioned complex questions, none of which can be adequately addressed by simple descriptive statistics.…”