Publication:
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

cris.virtual.department#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.department71261c1d-3be6-4c36-9c39-4994c79086fb
cris.virtualsource.orcid71261c1d-3be6-4c36-9c39-4994c79086fb
dc.contributor.affiliationUniversite de Lorraine; Dalian University of Technology; Centre National de la Recherche Scientifique (CNRS); Turkish Aeronautical Association; Turk Hava Kurumu University; Le Mans Universite
dc.contributor.authorLamirel, Jean-Charles; Chen, Yue; Cuxac, Pascal; Al Shehabi, Shadi; Dugue, Nicolas; Liu, Zeyuan
dc.date.accessioned2024-06-25T11:45:50Z
dc.date.available2024-06-25T11:45:50Z
dc.date.issued2020
dc.description.abstractIn 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.
dc.description.doi10.1007/s11192-020-03503-8
dc.description.endpage2999
dc.description.issue3
dc.description.pages29
dc.description.researchareasComputer Science; Information Science & Library Science
dc.description.startpage2971
dc.description.urihttp://dx.doi.org/10.1007/s11192-020-03503-8
dc.description.volume125
dc.description.woscategoryComputer Science, Interdisciplinary Applications; Information Science & Library Science
dc.identifier.issn0138-9130
dc.identifier.urihttps://acikarsiv.thk.edu.tr/handle/123456789/1344
dc.language.isoEnglish
dc.publisherSPRINGER
dc.relation.journalSCIENTOMETRICS
dc.subjectScience of Science; China; World; Topic tracking; Feature maximization; Unsupervised learning; Diachronic analysis
dc.titleAn 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
dc.typeArticle
dspace.entity.typePublication

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