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Technical report: the trend of author compound names and its implications for authorship identity identification

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  • Omar Hernando Avila-Poveda

    (Universidad del Mar (UMAR))

Abstract

Citation analysis has become an essential tool for research and academic effectiveness evaluation of universities. However, authorship identity has long been difficult to resolve in bibliometric analyses for many scientific fields, where performance of algorithms against human judgment is far from universal. Now with the boom of authors with compound names (mainly, Latino researchers and from Portuguese language countries) in scientific publications, clustering methods continue lowering their performance, due to completely forgetting the context and order of names (first name“s” and last name“s”) of each author in the publication (authorship identity). These kinds of mistakes affect visualization of publications, decreasing the likelihood of finding a given article by a specific author and generating bad quotations in the online systems. This has led to an unsuitable registration and unsuitable grouping of author names “ambiguous authorship identity” of each scientific publication. This process requires more work, time, attention, and accountability on the part of authors, reviewers, journal editors, and providers of bibliographic databases. These errors can be corrected by cross-referencing with each full original article, using manual checks and without ignoring the names issue at the moment of drafting and/or reviewing a manuscript. This paper seeks to raise awareness on how to write author names, highlighting the way in which they are being cited and self-citing the name of authors and co-authors in the publications.

Suggested Citation

  • Omar Hernando Avila-Poveda, 2014. "Technical report: the trend of author compound names and its implications for authorship identity identification," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 833-846, October.
    • Handle: RePEc:spr:scient:v:101:y:2014:i:1:d:10.1007_s11192-014-1359-7
    DOI: 10.1007/s11192-014-1359-7
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    References listed on IDEAS

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    1. Jian Wang & Kaspars Berzins & Diana Hicks & Julia Melkers & Fang Xiao & Diogo Pinheiro, 2012. "A boosted-trees method for name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 391-411, November.
    2. Zehra Taşkın & Umut Al, 2014. "Standardization problem of author affiliations in citation indexes," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 347-368, January.
    3. Jiang Wu & Xiu-Hao Ding, 2013. "Author name disambiguation in scientific collaboration and mobility cases," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(3), pages 683-697, September.
    4. Howard D. White, 2001. "Author-centered bibliometrics through CAMEOs: Characterizations automatically made and edited online," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(3), pages 607-637, July.
    5. Carmen Galvez & Félix Moya-Anegón, 2006. "The unification of institutional addresses applying parametrized finite-state graphs (P-FSG)," Scientometrics, Springer;Akadémiai Kiadó, vol. 69(2), pages 323-345, November.
    6. Timothy D. Fry & Joan M. Donohue, 2014. "Exploring the Author Affiliation Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1647-1667, March.
    7. Li Tang & John P. Walsh, 2010. "Bibliometric fingerprints: name disambiguation based on approximate structure equivalence of cognitive maps," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(3), pages 763-784, September.
    8. F. Collazo-Reyes & M. E. Luna-Morales & J. M. Russell & M. A. Pérez-Angón, 2008. "Publication and citation patterns of Latin American & Caribbean journals in the SCI and SSCI from 1995 to 2004," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(1), pages 145-161, April.
    9. José M. Soler, 2007. "Separating the articles of authors with the same name," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 281-290, August.
    10. Thomas Gurney & Edwin Horlings & Peter van den Besselaar, 2012. "Author disambiguation using multi-aspect similarity indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 435-449, May.
    11. Howard D. White, 2001. "Author-centered bibliometrics through CAMEOs: Characterizations automatically made and edited online," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 607-637, January.
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