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Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100

Author

Listed:
  • Nataliya Matveeva

    (National Research University Higher School of Economics)

  • Anuška Ferligoj

    (National Research University Higher School of Economics
    University of Ljubljana)

Abstract

Changes in patterns of collaboration between Russian universities after the commencement of the Russian university excellence initiative (Project 5-100) are studied in this paper. While this project aimed to make leading Russian universities more globally competitive and improve their research productivity, it also happened to increase their cooperation. An analysis of affiliations and the co-authorship networks was conducted to explore scientific collaborations between and within the participating universities. Such analysis facilitates the investigation of the number of collaborations with other organizations, both domestic and international cooperation, and disciplinary differences. By analyzing the co-authorship networks, the position of universities in the academic network and the structure of collaborations among the participants were examined. A sample of 30 Russian universities, including participants in Project 5-100 and a control group of institutions with similar characteristics, was used. After joining the project, the participating universities increased both their cooperation with each other as well as with foreign universities and research institutions of the Russian Academy of Sciences, especially in the high-quality segment. At the same time, the collaboration patterns of non-participating universities did not change significantly. The centrality of Project 5-100 universities in the global academic network has increased, along with their visibility and coupling in the national network. The historical division between university and academic sectors has diminished, while the participating universities have started to play a more important role in knowledge production within the country.

Suggested Citation

  • Nataliya Matveeva & Anuška Ferligoj, 2020. "Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2383-2407, September.
    • Handle: RePEc:spr:scient:v:124:y:2020:i:3:d:10.1007_s11192-020-03602-6
    DOI: 10.1007/s11192-020-03602-6
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    References listed on IDEAS

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    Cited by:

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    2. Hanna Hottenrott & Michael E. Rose & Cornelia Lawson, 2021. "The rise of multiple institutional affiliations in academia," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 1039-1058, August.
    3. Hui Dong & Dan Luo & Xudong Zeng & Zhentao Zou, 2023. "Cross‐affiliation collaboration and power laws for research output of institutions: Evidence and theory from top three finance journals," International Studies of Economics, John Wiley & Sons, vol. 18(4), pages 502-526, December.
    4. Hanna Hottenrott & Cornelia Lawson, 2022. "What is behind multiple institutional affiliations in academia?," Science and Public Policy, Oxford University Press, vol. 49(3), pages 382-402.
    5. Fan, Lingxu & Guo, Lei & Wang, Xinhua & Xu, Liancheng & Liu, Fangai, 2022. "Does the author’s collaboration mode lead to papers’ different citation impacts? An empirical analysis based on propensity score matching," Journal of Informetrics, Elsevier, vol. 16(4).

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    More about this item

    Keywords

    Social network analysis; Scientific collaboration; University excellence initiative; Co-authorship network; Blockmodeling; University; Russia;
    All these keywords.

    JEL classification:

    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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