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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

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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.

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Notes

  1. It is worth noting that very recent scientific contributions on Science of Science seem to indicate that the Science of Science research domain is re-broadening its scope to come back to the original Bernal’s paradigm. Examples of this are the works by Zeng et al. (2017) published in Physics Reports and Science by the System Science Research Team of Beijing Normal University in China, Fortunato et al. (2018) which was a collaboration project between authors from the University of Indiana (USA) and Leiden University (The Netherlands) or even recent high quality papers published by the Complex Networks Research Team of the Northeastern University (USA) (Huang et al. 2012; Wang and Barabási 2013; Shen and Barabási 2014; Sinatra et al. 2017).

  2. http://cluster.ischool.drexel.edu/~cchen/citespace/download/.

  3. See Acknowledgment section for more precise information on experts.

  4. Because the CNKI database was queried in Chinese, we use two different terms because "Science of Science" is described by two different terms in this language: "科学学" and "科学的科学".

  5. Chinese Social Sciences Citation Index.

  6. For articles prior to 1997 that did not contain a summary or keywords we only used the information in the title.

  7. http://ictclas.nlpir.org/.

  8. The frequency threshold of 6 was found empirically. It means the description space can be significantly reduced while allowing for accurate clustering (the quality of which was estimated both by the experts and by our quality measures presented in section "Clustering and optimal model detection"). No documents were deleted by this process.

  9. In this article, the features represent the words extracted from the title, abstract and keywords of the articles, the weights of the features are the adjusted frequency information associated with them and the unsupervised classification (clustering) is based on the GNG algorithm.

  10. A feature with negative values can be separated into 2 different positive sub-features without loss of information. The first represents the positive part of the original feature and the second its negative part.

  11. Behaviour of our measures is similar with classes or clusters.

  12. Like the Dunn index (Dunn 1974), the Davies-Bouldin index (Davies et Bouldin 1979), the Silhouette index (Rousseeuw 1987), the Caliński-Harabasz index (1974) or the Xie-Beni index (1991).

  13. In Eq. 7, labels represent categories or clusters to which data are associated.

  14. In many cases, each data can have several external labels of the same kind. For example, a research paper can have several different authors.

  15. In this case the opinion of our domain experts themselves (see "Acknowledgment" section for expert descriptions).

  16. Yang Xiaolin. Thirty years of science and Technology Policy Research—Wu Mingyu's oral autobiography [M]. Hunan Education Press 2015.

  17. From 2009, 5 training seminars on knowledge mapping were hold in WISELAB (Dalian University of Technology) broadcasting the methods and thinking widely in China. This approach also lead to the present paper using specific mapping tool to highlight the structure and the evolution of Science of Science domain in China.

  18. See section "Data collection and preprocessing" for more details.

  19. https://radimrehurek.com/gensim/.

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Acknowledgements

Two of the authors of this paper and an additional famous researcher play the role of the domain experts that have been mobilized for this work. Their help proved to be invaluable in carrying out our analysis of the results the proposed methodology: Professor Liu Zeyuan is one of the pioneers of Science and Science in China and one of the most important contributors to the field. He is one of the founders of the scientific societies in Science of Science in China and also the founder of WISELAB at Dalian University, a laboratory itself a pioneer in the quantitative study of Science of Science in China. Professor Liming Liang is one of the pioneer researchers in Scientometrics in China. She represents the Chinese researcher with the largest international academic reputation in that domain. Professor Chen Yue is the current director of WISELAB. Under his influence, this laboratory developed the use of modern methods of science analysis, especially cartographic methods. Today, this laboratory is considered one of the three main Chinese laboratories in the field of Science and Science. We also wish to warmly thank Richard Dickinson for the careful proof-reading of our paper.

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Authors and Affiliations

  1. SYNALP Team-LORIA, INRIA Nancy-Grand Est, Vandoeuvre-lès-Nancy, France

    Jean-Charles Lamirel

  2. WISELAB, Dalian University of Technology, Dalian, China

    Yue Chen & Zeyuan Liu

  3. INIST-CNRS, Vandoeuvre-lès-Nancy, France

    Pascal Cuxac

  4. University of Turkish Aeronautical Association, Ankara, Turkey

    Shadi Al Shehabi

  5. LIUM, Université du Mans, Le Mans, France

    Nicolas Dugué

Authors
  1. Jean-Charles Lamirel
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  2. Yue Chen
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  3. Pascal Cuxac
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  4. Shadi Al Shehabi
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  5. Nicolas Dugué
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  6. Zeyuan Liu
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Corresponding author

Correspondence to Jean-Charles Lamirel.

Additional information

Professor Liu Zeyuan—author of the paper decesased on Februray 8th, 2020, during the review process.

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Lamirel, JC., Chen, Y., Cuxac, P. et al. 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. Scientometrics 125, 2971–2999 (2020). https://doi.org/10.1007/s11192-020-03503-8

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