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Validity of citation criteria for assessing the influence of scientific publications: New evidence with peer assessment

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  • Stephen M. Lawani
  • Alan E. Bayer

Abstract

This article reviews the principal correlational studies employing citation counts as criterion measures for assessing the impact of scientific scholarship. The rationale and limitations of such measures and studies are discussed. New evidence on the validity of citation criteria is presented based on a sample of 870 cancer research papers, divided into three groups (“first‐order” papers, abstracted in the Year Book of Cancer; “second‐order” papers, listed but not abstracted in the yearbook; and “average‐order” papers, a representative cross section of research papers unlisted in the yearbook). Results consistently show that highly rated papers are more highly cited over the ensuing five years after publication, or when controls are introduced for self‐citations, for the influence of listing in the yearbook, and for language and country of authorship. The implications of results are discussed.

Suggested Citation

  • Stephen M. Lawani & Alan E. Bayer, 1983. "Validity of citation criteria for assessing the influence of scientific publications: New evidence with peer assessment," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 34(1), pages 59-66, January.
    • Handle: RePEc:bla:jamest:v:34:y:1983:i:1:p:59-66
    DOI: 10.1002/asi.4630340109
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    Cited by:

    1. Chung, Kee H. & Cox, Raymond A.K. & Kim, Kenneth A., 2009. "On the relation between intellectual collaboration and intellectual output: Evidence from the finance academe," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 893-916, August.
    2. Necmi K. Avkiran, 2013. "An empirical investigation of the influence of collaboration in Finance on article impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 911-925, June.
    3. Jiang, Zhuoren & Lin, Tianqianjin & Huang, Cui, 2023. "Deep representation learning of scientific paper reveals its potential scholarly impact," Journal of Informetrics, Elsevier, vol. 17(1).
    4. Pierre Azoulay & Danielle Li, 2020. "Scientific Grant Funding," NBER Chapters, in: Innovation and Public Policy, pages 117-150, National Bureau of Economic Research, Inc.
    5. Necmi Avkiran & Karen Alpert, 2015. "The influence of co-authorship on article impact in OR/MS/OM and the exchange of knowledge with Finance in the twenty-first century," Annals of Operations Research, Springer, vol. 235(1), pages 51-73, December.
    6. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    7. Siluo Yang & Feng Ma & Yanhui Song & Junping Qiu, 2010. "A longitudinal analysis of citation distribution breadth for Chinese scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 755-765, December.
    8. Ann Hillier & Ryan P Kelly & Terrie Klinger, 2016. "Narrative Style Influences Citation Frequency in Climate Change Science," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-12, December.
    9. Erjia Yan & Ying Ding & Qinghua Zhu, 2010. "Mapping library and information science in China: a coauthorship network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 83(1), pages 115-131, April.
    10. Pierre Azoulay & Danielle Li, 2020. "Scientific Grant Funding," NBER Working Papers 26889, National Bureau of Economic Research, Inc.

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