IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v128y2023i3d10.1007_s11192-023-04631-7.html
   My bibliography  Save this article

Evaluating scientists by citation and disruption of their representative works

Author

Listed:
  • Ruijie Wang

    (University of Fribourg)

  • Yuhao Zhou

    (University of Fribourg)

  • An Zeng

    (Beijing Normal University)

Abstract

A well-designed method for evaluating scientists is vital for the scientific community. It can be used to rank scientists in various practical tasks, such as hiring, funding application and promotion. However, a large number of evaluation methods are designed based on citation counts which can merely evaluate scientists’ scientific impact but can not evaluate their innovation ability which actually is a crucial characteristic for scientists. In addition, when evaluating scientists, it has become increasingly common to only focus on their representative works rather than all their papers. Accordingly, we here propose a hybrid method by combining scientific impact with innovation under representative works framework to evaluate scientists. Our results are validated on the American Physical Society journals dataset and the prestigious laureates datasets. The results suggest that the correlation between citation and disruption is weak, which enables us to incorporate them. In addition, the analysis shows that using representative works framework to evaluate scientists is advantageous and our hybrid method can effectively identify the Nobel Prize laureates and several other prestigious prizes laureates with higher precision and better mean ranking. The evaluation performance of the hybrid method is shown to be the best compared with the mainstream methods. This study provides policy makers an effective way to evaluate scientists from more comprehensive dimensions.

Suggested Citation

  • Ruijie Wang & Yuhao Zhou & An Zeng, 2023. "Evaluating scientists by citation and disruption of their representative works," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1689-1710, March.
  • Handle: RePEc:spr:scient:v:128:y:2023:i:3:d:10.1007_s11192-023-04631-7
    DOI: 10.1007/s11192-023-04631-7
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-023-04631-7
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11192-023-04631-7?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    2. Mariani, Manuel Sebastian & Medo, Matúš & Zhang, Yi-Cheng, 2016. "Identification of milestone papers through time-balanced network centrality," Journal of Informetrics, Elsevier, vol. 10(4), pages 1207-1223.
    3. Ying Ding, 2011. "Applying weighted PageRank to author citation networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(2), pages 236-245, February.
    4. Philip Ball, 2005. "Index aims for fair ranking of scientists," Nature, Nature, vol. 436(7053), pages 900-900, August.
    5. Pablo D. Batista & Mônica G. Campiteli & Osame Kinouchi, 2006. "Is it possible to compare researchers with different scientific interests?," Scientometrics, Springer;Akadémiai Kiadó, vol. 68(1), pages 179-189, July.
    6. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    7. Sune Lehmann & Andrew D. Jackson & Benny E. Lautrup, 2006. "Measures for measures," Nature, Nature, vol. 444(7122), pages 1003-1004, December.
    8. Subramanian, Bala, 2006. "Growth and its measurement," Journal of Financial Transformation, Capco Institute, vol. 17, pages 32-25.
    9. Blaise Cronin & Lokman Meho, 2006. "Using the h‐index to rank influential information scientistss," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(9), pages 1275-1278, July.
    10. Nanak Kakwani & Hyun H. Son, 2006. "A note on measuring unemployment," Working Papers 28, International Policy Centre for Inclusive Growth.
    11. Jianlin Zhou & An Zeng & Ying Fan & Zengru Di, 2018. "Identifying important scholars via directed scientific collaboration networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1327-1343, March.
    12. Oecd, 2006. "Risk Effects of PSE Crop Measures," OECD Papers, OECD Publishing, vol. 5(11), pages 1-49.
    13. Nykl, Michal & Ježek, Karel & Fiala, Dalibor & Dostal, Martin, 2014. "PageRank variants in the evaluation of citation networks," Journal of Informetrics, Elsevier, vol. 8(3), pages 683-692.
    14. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    15. Kaur, Jasleen & Radicchi, Filippo & Menczer, Filippo, 2013. "Universality of scholarly impact metrics," Journal of Informetrics, Elsevier, vol. 7(4), pages 924-932.
    16. Lutz Bornmann & Alexander Tekles, 2019. "Disruptive papers published in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 331-336, July.
    17. Xiaodan Zhu & Peter Turney & Daniel Lemire & André Vellino, 2015. "Measuring academic influence: Not all citations are equal," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(2), pages 408-427, February.
    18. Chen, P. & Xie, H. & Maslov, S. & Redner, S., 2007. "Finding scientific gems with Google’s PageRank algorithm," Journal of Informetrics, Elsevier, vol. 1(1), pages 8-15.
    19. Niu, Qikai & Zhou, Jianlin & Zeng, An & Fan, Ying & Di, Zengru, 2016. "Which publication is your representative work?," Journal of Informetrics, Elsevier, vol. 10(3), pages 842-853.
    20. Ying Ding, 2011. "Applying weighted PageRank to author citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(2), pages 236-245, February.
    21. Qurat-ul Ain & Hira Riaz & Muhammad Tanvir Afzal, 2019. "Evaluation of h-index and its citation intensity based variants in the field of mathematics," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 187-211, April.
    22. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    23. Leo Egghe, 2006. "Theory and practise of the g-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(1), pages 131-152, October.
    24. John P. A. Ioannidis & Kevin W. Boyack & Henry Small & Aaron A. Sorensen & Richard Klavans, 2014. "Bibliometrics: Is your most cited work your best?," Nature, Nature, vol. 514(7524), pages 561-562, October.
    25. Hyun H. Son & Nanak Kakwani, 2006. "Measuring the Impact of Price Changes on Poverty," Working Papers 33, International Policy Centre for Inclusive Growth.
    26. Lutz Bornmann & Sitaram Devarakonda & Alexander Tekles & George Chacko, 2020. "Disruptive papers published in Scientometrics: meaningful results by using an improved variant of the disruption index originally proposed by Wu, Wang, and Evans (2019)," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 1149-1155, May.
    27. Fang Zhang & Shengli Wu, 2021. "Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7197-7222, August.
    28. Fiala, Dalibor & Šubelj, Lovro & Žitnik, Slavko & Bajec, Marko, 2015. "Do PageRank-based author rankings outperform simple citation counts?," Journal of Informetrics, Elsevier, vol. 9(2), pages 334-348.
    29. Hui, C.H. & Lo, C.F. & Wong, T.C. & Man, P.K., 2006. "Measuring provisions for collateralised retail lending," Journal of Economics and Business, Elsevier, vol. 58(4), pages 343-361.
    30. An Zeng & Ying Fan & Zengru Di & Yougui Wang & Shlomo Havlin, 2021. "Fresh teams are associated with original and multidisciplinary research," Nature Human Behaviour, Nature, vol. 5(10), pages 1314-1322, October.
    31. Sotaro Shibayama & Jian Wang, 2020. "Measuring originality in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 409-427, January.
    32. Hao Wang & Hua-Wei Shen & Xue-Qi Cheng, 2016. "Scientific credit diffusion: Researcher level or paper level?," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 827-837, November.
    33. Jianlin Zhou & An Zeng & Ying Fan & Zengru Di, 2018. "The representative works of scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1721-1732, December.
    34. Lutz Bornmann & Hans-Dieter Daniel, 2005. "Does the h-index for ranking of scientists really work?," Scientometrics, Springer;Akadémiai Kiadó, vol. 65(3), pages 391-392, December.
    35. Ruan, Xuanmin & Lyu, Dongqing & Gong, Kaile & Cheng, Ying & Li, Jiang, 2021. "Rethinking the disruption index as a measure of scientific and technological advances," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    36. Siying Li & Huawei Shen & Peng Bao & Xueqi Cheng, 2021. "$$h_u$$ h u -index: a unified index to quantify individuals across disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3209-3226, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanbo Zhou & Xin-Li Xu & Xu-Hua Yang & Qu Li, 2022. "The influence of disruption on evaluating the scientific significance of papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5931-5945, October.
    2. Asma Hammami & Nabil Semmar, 2022. "The simplex simulation as a tool to reveal publication strategies and citation factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 319-350, January.
    3. Karol Flores-Szwagrzak & Rafael Treibich, 2020. "Teamwork and Individual Productivity," Management Science, INFORMS, vol. 66(6), pages 2523-2544, June.
    4. Dinesh Pradhan & Partha Sarathi Paul & Umesh Maheswari & Subrata Nandi & Tanmoy Chakraborty, 2017. "$$C^3$$ C 3 -index: a PageRank based multi-faceted metric for authors’ performance measurement," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 253-273, January.
    5. Maria-Victoria Uribe-Bohorquez & Juan-Camilo Rivera-Ordóñez & Isabel-María García-Sánchez, 2023. "Gender disparities in accounting academia: analysis from the lens of publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(7), pages 3827-3865, July.
    6. Libo Sheng & Dongqing Lyu & Xuanmin Ruan & Hongquan Shen & Ying Cheng, 2023. "The association between prior knowledge and the disruption of an article," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4731-4751, August.
    7. Guoliang Lyu & Ganwei Shi, 2019. "On an approach to boosting a journal’s citation potential," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1387-1409, September.
    8. Xing, Yanmeng & Wang, Fenghua & Zeng, An & Ying, Fan, 2021. "Solving the cold-start problem in scientific credit allocation," Journal of Informetrics, Elsevier, vol. 15(3).
    9. Yanan Wang & An Zeng & Ying Fan & Zengru Di, 2019. "Ranking scientific publications considering the aging characteristics of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 155-166, July.
    10. van Eck, Nees Jan & Waltman, Ludo, 2008. "Generalizing the h- and g-indices," Journal of Informetrics, Elsevier, vol. 2(4), pages 263-271.
    11. Zhou, Yuhao & Wang, Ruijie & Zeng, An & Zhang, Yi-Cheng, 2020. "Identifying prize-winning scientists by a competition-aware ranking," Journal of Informetrics, Elsevier, vol. 14(3).
    12. Siying Li & Huawei Shen & Peng Bao & Xueqi Cheng, 2021. "$$h_u$$ h u -index: a unified index to quantify individuals across disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3209-3226, April.
    13. Bornmann, Lutz & Tekles, Alexander, 2021. "Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts," Journal of Informetrics, Elsevier, vol. 15(3).
    14. van Eck, N.J.P. & Waltman, L., 2008. "Generalizing the h- and g-indices," ERIM Report Series Research in Management ERS-2008-049-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    15. Nykl, Michal & Campr, Michal & Ježek, Karel, 2015. "Author ranking based on personalized PageRank," Journal of Informetrics, Elsevier, vol. 9(4), pages 777-799.
    16. Zhang, Lin & Thijs, Bart & Glänzel, Wolfgang, 2011. "The diffusion of H-related literature," Journal of Informetrics, Elsevier, vol. 5(4), pages 583-593.
    17. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2018. "Author ranking evaluation at scale," Journal of Informetrics, Elsevier, vol. 12(3), pages 679-702.
    18. Nan Deng & An Zeng, 2023. "Enhancing the robustness of the disruption metric against noise," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2419-2428, April.
    19. Deming Lin & Tianhui Gong & Wenbin Liu & Martin Meyer, 2020. "An entropy-based measure for the evolution of h index research," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2283-2298, December.
    20. Ying Guo & Xiantao Xiao, 2022. "Author-level altmetrics for the evaluation of Chinese scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 973-990, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:128:y:2023:i:3:d:10.1007_s11192-023-04631-7. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.