Springer Research on Translator and Interpreter Training: A Collective Volume of Bibliometric Reviews and Empirical Studies on Learners (New Frontiers in Translation Studies)

Product Description This book comprehensively examines the development of translator and interpreter training using bibliometric reviews of the state of the field and empirical studies on classroom practice. It starts by introducing databases in bibliometric reviews and presents a detailed account of the reasons behind the project and its objectives as well as a description of the methods of constructing databases. The introduction is followed by full-scale review studies on various aspects of translator and interpreter training, providing not only an overall picture of the research themes and methods, but also valuable information on active authors, institutions and countries in the subfields of translator training, interpreter training, and translator and interpreter training in general. The book also compares publications from different subfields of research, regions and journals to show the special features within this discipline. Further, it provides a series of empirical studies conducted by the authors, covering a wide array of topics in translator and interpreter training, with an emphasis on learner factors.  This collective volume, with its unique perspective on bibliometric data and empirical studies, highlights the latest development in the field of translator and interpreter training research. The findings presented will help researchers, trainers and practitioners to reflect on the important issues in the discipline and find possible new directions for future research. From the Back Cover This book comprehensively examines the development of translator and interpreter training using bibliometric reviews of the state of the field and empirical studies on classroom practice. It starts by introducing databases in bibliometric reviews and presents a detailed account of the reasons behind the project and its objectives as well as a description of the methods of constructing databases. The introduction is followed by full-scale review studies on various aspects of translator and interpreter training, providing not only an overall picture of the research themes and methods, but also valuable information on active authors, institutions and countries in the subfields of translator training, interpreter training, and translator and interpreter training in general. The book also compares publications from different subfields of research, regions and journals to show the special features within this discipline. Further, it provides a series of empirical studies conducted by the authors, covering a wide array of topics in translator and interpreter training, with an emphasis on learner factors.  This collective volume, with its unique perspective on bibliometric data and empirical studies, highlights the latest development in the field of translator and interpreter training research. The findings presented will help researchers, trainers and practitioners to reflect on the important issues in the discipline and find possible new directions for future research. About the Author Dr. Jackie Xiu Yan is an associate professor at the Department of Linguistics and Translation at City University of Hong Kong. She received her PhD and MEd from the University of Texas at Austin, and her BA from Shanghai International Studies University.  Her research interests include social and affective factors in translation and language learning, corpus-based analysis of translation studies, as well as classical Chinese translation and interpretation. She has published widely in the fields of translation studies and applied linguistics. She is also a dedicated editor of a series of translation books and one of the contributing editors of the Chinese-English Dictionary by Fudan University Press published in 2015.  Dr. Jun Pan is an assistant professor in the Translation Programme at Hong Kong Baptist University. Holding a BA (Hons) from Jiangsu University, MA from Shanghai Internation