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Regularity in the time-dependent distribution of the percentage of never-cited papers: An empirical pilot study based on the six journals

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  • Hu, Zewen
  • Wu, Yishan

Abstract

The non-citation rate refers to the proportion of papers that do not attract any citation over a period of time following their publication. After reviewing all the related papers in Web of Science, Google Scholar and Scopus database, we find the current literature on citation distribution gives more focus on the distribution of the percentages and citations of papers receiving at least one citation, while there are fewer studies on the time-dependent patterns of the percentage of never-cited papers, on what distribution model can fit their time-dependent patterns, as well as on the factors influencing the non-citation rate. Here, we perform an empirical pilot analysis to the time-dependent distribution of the percentages of never-cited papers in a series of different, consecutive citation time windows following their publication in our selected six sample journals, and study the influence of paper length on the chance of papers’ getting cited. Through the above analysis, the following general conclusions are drawn: (1) a three-parameter negative exponential model can well fit time-dependent distribution curve of the percentages of never-cited papers; (2) in the initial citation time window, the percentage of never-cited papers in each journal is very high. However, as the citation time window becomes wider and wider, the percentage of never-cited papers begins to drop rapidly at first, and then drop more slowly, and the total degree of decline for most of journals is very large; (3) when applying the wider citation time windows, the percentage of never-cited papers for each journal begins to approach a stable value, and after that value, there will be very few changes in these stable percentages, unless we meet a large amount of “Sleeping Beauties” type papers; (4) the length of an paper has a great influence on whether it will be cited or not.

Suggested Citation

  • Hu, Zewen & Wu, Yishan, 2014. "Regularity in the time-dependent distribution of the percentage of never-cited papers: An empirical pilot study based on the six journals," Journal of Informetrics, Elsevier, vol. 8(1), pages 136-146.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:1:p:136-146
    DOI: 10.1016/j.joi.2013.11.002
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    Cited by:

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    5. Li, Jiang & Shi, Dongbo & Zhao, Star X. & Ye, Fred Y., 2014. "A study of the “heartbeat spectra” for “sleeping beauties”," Journal of Informetrics, Elsevier, vol. 8(3), pages 493-502.
    6. Hou, Jianhua & Yang, Xiucai, 2020. "Social media-based sleeping beauties: Defining, identifying and features," Journal of Informetrics, Elsevier, vol. 14(2).
    7. Aurora A. C. Teixeira & Pedro Cosme Vieira & Ana Patrícia Abreu, 2017. "Sleeping Beauties and their princes in innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 541-580, February.
    8. Zewen Hu & Yishan Wu & Jianjun Sun, 2018. "A quantitative analysis of determinants of non-citation using a panel data model," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 843-861, August.
    9. Marcel Clermont & Johanna Krolak & Dirk Tunger, 2021. "Does the citation period have any effect on the informative value of selected citation indicators in research evaluations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1019-1047, February.
    10. Liang, Liming & Zhong, Zhen & Rousseau, Ronald, 2015. "Uncited papers, uncited authors and uncited topics: A case study in library and information science," Journal of Informetrics, Elsevier, vol. 9(1), pages 50-58.
    11. Ji-ping Gao & Cheng Su & Hai-yan Wang & Li-hua Zhai & Yun-tao Pan, 2019. "Research fund evaluation based on academic publication output analysis: the case of Chinese research fund evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 959-972, May.
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    15. Pablo Dorta-González & Rafael Suárez-Vega & María Isabel Dorta-González, 2020. "Open access effect on uncitedness: a large-scale study controlling by discipline, source type and visibility," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2619-2644, September.
    16. Zewen Hu & Angela Lin & Peter Willett, 2019. "Identification of research communities in cited and uncited publications using a co-authorship network," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 1-19, January.

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