Title
How to Investigate the Historical Roots and Evolution of Research Fields in China? A Case Study on iMetrics Using RootCite
Abstract
This paper aimed to provide an approach to investigate the historical roots and evolution of research fields in China by extending the reference spectroscopy year spectroscopy (RPYS). RootCite, an open source software accepts raw data from both the Web of Science and the China Social Science Citation Index (CSSCI), was developed using python. We took iMetrics in China as the research case. 5141 Chinese iMetrics related publications with 73 376 non-distinct cited references (CR) collected from the CSSCI were analyzed using RootCite. The results showed that the first CR in the field can be dated back to 1882 and written in English; but the majority (64.2%) of the CR in the field were Chinese publications. 17 peaks referring to 18 seminal works (13 in English and 5 in Chinese) were located during the period from 1900 to 2017. The field shared the same roots with that in the English world (e.g., Lotka's law and Garfield's "Citation Indexes") but has its own characteristics, and it was then shaped by contributions from both the English world (e.g., Small's "Co-citation" and Callon et al.'s "Co-word analysis") and China (e.g., Qiu's "Bibliometrics" and Su's "CSSCI"). The three Chinese works have played irreplaceable and positive roles in the historical evolutionary path of the field, which should not be ignored, especially for the evolution of the field. This research demonstrated how RootCite aided the task of identifying the origin and evolution of research fields in China, which could be valuable for extending RPYS for countries with other languages.
Year
DOI
Venue
2020
10.1007/s11192-020-03659-3
SCIENTOMETRICS
Keywords
DocType
Volume
iMetrics in China,Reference publication year spectroscopy,RPYS,RootCite,Algorithm historiography,China Social Science Index (CSSCI)
Journal
125.0
Issue
ISSN
Citations 
2.0
0138-9130
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Xin Li110.96
Qiang Yao200.34
Xuli Tang300.34
Qian Li400.34
Mengjia Wu510.69