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Measuring delayed recognition for papers: Uneven weighted summation and total citations

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  • Min, Chao
  • Sun, Jianjun
  • Pei, Lei
  • Ding, Ying

Abstract

This paper studies the so-called abnormal phenomenon of delayed recognition in bibliometrics and focuses on the first step in quantitatively measuring this phenomenon. As bibliometric analysis of a paper’s recognition and influence is an uncertain and extended process, proper calculation of delayed recognition and “sleeping beauty” publications has limitations in current scientometric studies, such as restricted application indicators, scope, and complex calculation methods. This study suggests a solution for depicting the citation delay phenomenon of individual papers that avoids dividing them into different periods, is applicable to all papers with various types of citation curves, and is easy to calculate. Notably, this approach advocates using an uneven weighted summation based on earlier and later citation years when analyzing an individual paper’s citation data. It demonstrates that the intrinsic relation between two independent indicators of citation delay and Gs index is based on the same logic of applying uneven weights to sum up yearly citations. This paper also recommends that simultaneous application of the new indicator Da and final citation numbers can efficiently identify those delayed recognition papers, and that the criterion for selecting papers can be adjusted by the value of a.

Suggested Citation

  • Min, Chao & Sun, Jianjun & Pei, Lei & Ding, Ying, 2016. "Measuring delayed recognition for papers: Uneven weighted summation and total citations," Journal of Informetrics, Elsevier, vol. 10(4), pages 1153-1165.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:4:p:1153-1165
    DOI: 10.1016/j.joi.2016.10.001
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    Cited by:

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    2. Ratnadeep Dey & Anurag Roy & Tanmoy Chakraborty & Saptarshi Ghosh, 2017. "Sleeping beauties in Computer Science: characterization and early identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1645-1663, December.
    3. Zhichao Fang & Rodrigo Costas, 2020. "Studying the accumulation velocity of altmetric data tracked by Altmetric.com," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 1077-1101, May.
    4. József Popp & Péter Balogh & Judit Oláh & Sebastian Kot & Mónika Harangi Rákos & Péter Lengyel, 2018. "Social Network Analysis of Scientific Articles Published by Food Policy," Sustainability, MDPI, vol. 10(3), pages 1-20, February.
    5. Hui Fang, 2019. "A transition stage co-citation criterion for identifying the awakeners of sleeping beauty publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 307-322, October.
    6. Zhu, Wanying & Jin, Ching & Ma, Yifang & Xu, Cong, 2023. "Earlier recognition of scientific excellence enhances future achievements and promotes persistence," Journal of Informetrics, Elsevier, vol. 17(2).
    7. Lutz Bornmann & Adam Y. Ye & Fred Y. Ye, 2017. "Sequence analysis of annually normalized citation counts: an empirical analysis based on the characteristic scores and scales (CSS) method," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1665-1680, December.
    8. ZhangJian Zong & XuanZhen Liu & Hui Fang, 2018. "Sleeping beauties with no prince based on the co-citation criterion," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(3), pages 1841-1852, December.

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