An overview of the history of Science of Science in China based on the use of bibliographic and citation data: a new method of analysis based on clustering with feature maximization and contrast graphs.

Autor/inn/en	Lamirel, Jean-Charles; Chen, Yue; Cuxac, Pascal; Al Shehabi, Shadi; Dugué, Nicolas; Liu, Zeyuan
Titel	An overview of the history of Science of Science in China based on the use of bibliographic and citation data: a new method of analysis based on clustering with feature maximization and contrast graphs.
Quelle	In: Scientometrics, (2020) 3, S.2971-2999 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-020-03503-8
Schlagwörter	Science of Science; China; World; Topic tracking; Feature maximization; Unsupervised learning; Diachronic analysis
Abstract	Abstract In the first part of this paper, we shall discuss the historical context of Science of Science both in China and at world level. In the second part, we use the unsupervised combination of GNG clustering with feature maximization metrics and associated contrast graphs to present an analysis of the contents of selected academic journal papers in Science of Science in China and the construction of an overall map of the research topics’ structure during the last 40 years. Furthermore, we highlight how the topics have evolved through analysis of publication dates and also use author information to clarify the topics’ content. The results obtained have been reviewed and approved by 3 leading experts in this field and interestingly show that Chinese Science of Science has gradually become mature in the last 40 years, evolving from the general nature of the discipline itself to related disciplines and their potential interactions, from qualitative analysis to quantitative and visual analysis, and from general research on the social function of science to its more specific economic function and strategic function studies. Consequently, the proposed novel method can be used without supervision, parameters and help from any external knowledge to obtain very clear and precise insights about the development of a scientific domain. The output of the topic extraction part of the method (clustering + feature maximization) is finally compared with the output of the well-known LDA approach by experts in the domain which serves to highlight the very clear superiority of the proposed approach.
Erfasst von	OLC
Update	2023/2/05