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Citations versus expert opinions: citation analysis of featured reviews of the American Mathematical Society

Author

Listed:
  • Lawrence Smolinsky

    (Louisiana State University)

  • Daniel S. Sage

    (Louisiana State University)

  • Aaron J. Lercher

    (Louisiana State University)

  • Aaron Cao

    (Carnegie Mellon University)

Abstract

Peer review and citation metrics are two means of gauging the value of scientific research, but the lack of publicly available peer review data makes the comparison of these methods difficult. Mathematics can serve as a useful laboratory for considering these questions because as an exact science, there is a narrow range of reasons for citations. In mathematics, virtually all published articles are post-publication reviewed by mathematicians in Mathematical Reviews (MathSciNet) and so the data set was essentially the Web of Science mathematics publications from 1993 to 2004. For a decade, especially important articles were singled out in Mathematical Reviews for featured reviews. In this study, we analyze the bibliometrics of elite articles selected by peer review and by citation count. We conclude that the two notions of significance described by being a featured review article and being highly cited are distinct. This indicates that peer review and citation counts give largely independent determinations of highly distinguished articles. We also consider whether hiring patterns of subfields and mathematicians’ interest in subfields reflect subfields of featured review or highly cited articles. We re-examine data from two earlier studies in light of our methods for implications on the peer review/citation count relationship to a diversity of disciplines.

Suggested Citation

  • Lawrence Smolinsky & Daniel S. Sage & Aaron J. Lercher & Aaron Cao, 2021. "Citations versus expert opinions: citation analysis of featured reviews of the American Mathematical Society," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 3853-3870, May.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:5:d:10.1007_s11192-021-03894-2
    DOI: 10.1007/s11192-021-03894-2
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    References listed on IDEAS

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    1. Lawrence Smolinsky & Aaron Lercher & Andrew McDaniel, 2015. "Testing theories of preferential attachment in random networks of citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(10), pages 2132-2145, October.
    2. Stephen J. Bensman & Lawrence J. Smolinsky & Alexander I. Pudovkin, 2010. "Mean citation rate per article in mathematics journals: Differences from the scientific model," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(7), pages 1440-1463, July.
    3. Ludo Waltman & Rodrigo Costas, 2014. "F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 433-445, March.
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    6. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    7. Michael H. MacRoberts & Barbara R. MacRoberts, 2018. "The mismeasure of science: Citation analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(3), pages 474-482, March.
    8. Michael S. Patterson & Simon Harris, 2009. "The relationship between reviewers’ quality-scores and number of citations for papers published in the journal Physics in Medicine and Biology from 2003–2005," Scientometrics, Springer;Akadémiai Kiadó, vol. 80(2), pages 343-349, August.
    9. Bornmann, Lutz & Osório, António, 2019. "The value and credits of n-authors publications," Journal of Informetrics, Elsevier, vol. 13(2), pages 540-554.
    10. Dag W. Aksnes & Liv Langfeldt & Paul Wouters, 2019. "Citations, Citation Indicators, and Research Quality: An Overview of Basic Concepts and Theories," SAGE Open, , vol. 9(1), pages 21582440198, February.
    11. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    12. Stephen J. Bensman & Lawrence J. Smolinsky & Alexander I. Pudovkin, 2010. "Mean citation rate per article in mathematics journals: Differences from the scientific model," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(7), pages 1440-1463, July.
    13. Jacques Wainer & Paula Vieira, 2013. "Correlations between bibliometrics and peer evaluation for all disciplines: the evaluation of Brazilian scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 395-410, August.
    14. Lawrence Smolinsky & Aaron Lercher, 2012. "Citation rates in mathematics: a study of variation by subdiscipline," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 911-924, June.
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    1. Weixi Xie & Pengfei Jia & Guangyao Zhang & Xianwen Wang, 2024. "Are reviewer scores consistent with citations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(8), pages 4721-4740, August.
    2. Lin Zhang & Ziyi Tu & Yifei Yu & Yuanyuan Shang & Ying Huang, 2024. "Spotting potential reviewers for interdisciplinary research: insights on active reviewers from Publons," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5533-5556, September.

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