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Deep and narrow impact: introducing location filtered citation counting

Author

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  • Dangzhi Zhao

    (University of Alberta)

  • Andreas Strotmann

    (ScienceXplore)

Abstract

The present study tests a citation counting method that filters out citations in the introductory and backgrounds sections and then weighs the remaining citations by their in-text frequency. The dataset used comprises articles on bibliometrics available in full text in PubMed Central. This method was inspired by findings from previous studies that in-text frequency indicates importance of citations and citations in Methodology, Results, Discussion, and Conclusions sections tend to be more important to a citing article. We found that this method makes a large difference in author ranking as suggested by a 0.4 correlation between ranking by this method and that by traditional citation counting. Generally, this method has ranked authors concerning biomedical issues higher and those focused on bibliometrics or science communication issues lower compared to traditional citation counting. This rank change pattern suggests that this method appears to have made essential citations stand out more, i.e., citations that studies concerning biomedicine are expected to draw on more heavily. This method has also ranked guidelines or theoretical or methodological frameworks for systematic reviews, meta-analyses, knowledge translation, and scoping studies much higher, indicating that Bibliometrics has been mostly employed in these types of studies in biomedical fields. Unfortunately, citation network analysis doesn’t seem to have been employed much as indicated by key authors representing science mapping being ranked much lower by this method although it has been shown to be informative for these types of studies.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:122:y:2020:i:1:d:10.1007_s11192-019-03280-z
    DOI: 10.1007/s11192-019-03280-z
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    2. Orlando Fonseca Guilarte & Simone Diniz Junqueira Barbosa & Sinesio Pesco, 2021. "RelPath: an interactive tool to visualize branches of studies and quantify the expertise of authors by citation paths," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 4871-4897, June.
    3. Dongqing Lyu & Xuanmin Ruan & Juan Xie & Ying Cheng, 2021. "The classification of citing motivations: a meta-synthesis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3243-3264, April.
    4. Indra Budi & Yaniasih Yaniasih, 2023. "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 735-759, January.
    5. 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.
    6. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2021. "An in-text citation classification predictive model for a scholarly search system," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5509-5529, July.

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