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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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    References listed on IDEAS

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    3. 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.
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    7. Li, Xin & Ma, Xiaodi & Feng, Ye, 2024. "Early identification of breakthrough research from sleeping beauties using machine learning," Journal of Informetrics, Elsevier, vol. 18(2).
    8. 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.
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