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Quantifying perceived impact of scientific publications

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  • Radicchi, Filippo
  • Weissman, Alexander
  • Bollen, Johan

Abstract

We report on an empirical verification of the degree to which citation numbers represent scientific impact as it is actually perceived by experts in their respective field. We run a survey of about 2000 corresponding authors who performed a pairwise impact assessment task across more than 20,000 scientific articles. Results of the survey show that citation data and perceived impact do not align well, unless one properly accounts for psychological biases that affect the opinions of experts with respect to their own papers vs. those of others. Researchers tend to prefer their own publications to the most cited papers in their field of research. There is only a mild positive correlation between the number of citations of top-cited papers and expert preference in pairwise comparisons. This also applies to pairs of papers with several orders of magnitude differences in their total number of accumulated citations. However, when researchers were asked to choose among pairs of their own papers, thus eliminating the bias favouring one's own papers over those of others, they did systematically prefer the most cited article. We conclude that, when scientists have full information and are making unbiased choices, expert opinion on impact is congruent with citation numbers.

Suggested Citation

  • Radicchi, Filippo & Weissman, Alexander & Bollen, Johan, 2017. "Quantifying perceived impact of scientific publications," Journal of Informetrics, Elsevier, vol. 11(3), pages 704-712.
  • Handle: RePEc:eee:infome:v:11:y:2017:i:3:p:704-712
    DOI: 10.1016/j.joi.2017.05.010
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    1. Isager, Peder Mortvedt & van 't Veer, Anna Elisabeth & Lakens, Daniel, 2021. "Replication value as a function of citation impact and sample size," MetaArXiv knjea, Center for Open Science.
    2. 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.
    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. Teplitskiy, Misha & Duede, Eamon & Menietti, Michael & Lakhani, Karim R., 2022. "How status of research papers affects the way they are read and cited," Research Policy, Elsevier, vol. 51(4).
    5. Kiran Sharma & Parul Khurana, 2021. "Growth and dynamics of Econophysics: a bibliometric and network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4417-4436, May.
    6. Zhiya Zuo & Kang Zhao, 2021. "Understanding and predicting future research impact at different career stages—A social network perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(4), pages 454-472, April.

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