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Can retrieval of information from citation indexes be simplified? Multiple mention of a reference as a characteristic of the link between cited and citing article

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  • Gertrud Herlach

Abstract

The hypothesis is tested and accepted that the mechanistically identifiable citation link characteristic, mention of a given reference more than once within the same research paper, indicates a close and useful relationship of a citing to a given cited paper. Closeness and usefulness of the relationship between papers linked by citation were determined by means of users' judgments. It is shown that as a selection criterion for document retrieval, multiple mention of a reference would yield good precision but low recall, since a considerable number of papers with corresponding single mention were judged closely related to the given cited paper. Frequency counts showed that approximately one‐third of all bibliographic references in the research papers checked are mentioned in the text more than once.

Suggested Citation

  • Gertrud Herlach, 1978. "Can retrieval of information from citation indexes be simplified? Multiple mention of a reference as a characteristic of the link between cited and citing article," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 29(6), pages 308-310, November.
    • Handle: RePEc:bla:jamest:v:29:y:1978:i:6:p:308-310
    DOI: 10.1002/asi.4630290608
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    Cited by:

    1. Ding, Ying & Liu, Xiaozhong & Guo, Chun & Cronin, Blaise, 2013. "The distribution of references across texts: Some implications for citation analysis," Journal of Informetrics, Elsevier, vol. 7(3), pages 583-592.
    2. Matthias Sebastian Rüdiger & David Antons & Torsten-Oliver Salge, 2021. "The explanatory power of citations: a new approach to unpacking impact in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9779-9809, December.
    3. Dangzhi Zhao & Andreas Strotmann, 2020. "Deep and narrow impact: introducing location filtered citation counting," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 503-517, January.
    4. Liyue Chen & Jielan Ding & Vincent Larivière, 2022. "Measuring the citation context of national self‐references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(5), pages 671-686, May.
    5. Dangzhi Zhao & Andreas Strotmann, 2020. "Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 255-270, July.
    6. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    7. Pak, Chol Myong & Wang, Weibin & Yu, Guang, 2020. "An analysis of in-text citations based on fractional counting," Journal of Informetrics, Elsevier, vol. 14(4).
    8. Weibin Wang & Zheng Wang & Tian Yu & CholMyong Pak & Guang Yu, 2020. "Research on citation mention times and contributions using a neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2383-2400, December.
    9. Zehra Taşkın & Umut Al, 2018. "A content-based citation analysis study based on text categorization," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 335-357, January.

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