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Co-author inclusion: A novel recursive algorithmic method for dealingwith homonyms in bibliometric analysis

Author

Listed:
  • Steven Wooding

    (RAND Europe)

  • Kate Wilcox-Jay

    (Department of Information Science, City University)

  • Grant Lewison

    (Department of Information Science, City University)

  • Jonathan Grant

    (RAND Europe)

Abstract

Summary Large scale bibliometric analysis is often hindered by the presence of homonyms, or namesakes, of the researchers of interest in literature databases. This makes it difficult to build up a true picture of a researcher's publication record, as publications by another researcher with the same name will be included in search results. Using additional information such as title and author addresses, an expert in the field can generally tell if a paper is by a researcher or a namesake; however, manual checking is not practical in large scale studies. Previously various methods have been used to address this problem, chiefly based on filtering by subject, funding acknowledgement or author address. Co-author inclusion is a novel algorithmic method based on co-authorship for dealing with problems of homonyms in large bibliometric surveys. We compared co-author inclusion and subject and funding based filter against the manual assignment of papers by a subject expert (which we assumed to be correct). The subject and funding based filtering identifies only 75% as many papers as assigned by manual scoring. By using co-author inclusion once we increase this to 95%, two further rounds produces 99% as many papers as manual filtering. Although the number of papers identified that were not assigned to the PIs manually also increases, the absolute number is low: rising from 0.2% papers with subject and funding filtering, to 3% papers for three rounds of co-author inclusion.

Suggested Citation

  • Steven Wooding & Kate Wilcox-Jay & Grant Lewison & Jonathan Grant, 2006. "Co-author inclusion: A novel recursive algorithmic method for dealingwith homonyms in bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 66(1), pages 11-21, January.
  • Handle: RePEc:spr:scient:v:66:y:2006:i:1:d:10.1007_s11192-006-0002-7
    DOI: 10.1007/s11192-006-0002-7
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    Citations

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    Cited by:

    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. Cathelijn J F Waaijer & Benoît Macaluso & Cassidy R Sugimoto & Vincent Larivière, 2016. "Stability and Longevity in the Publication Careers of U.S. Doctorate Recipients," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-15, April.
    3. José M. Soler, 2007. "Separating the articles of authors with the same name," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(2), pages 281-290, August.
    4. Lowe, D. Jordan & Van Fleet, David D., 2009. "Scholarly achievement and accounting journal editorial board membership," Journal of Accounting Education, Elsevier, vol. 27(4), pages 197-209.
    5. Emilio Abad-Segura & Mariana-Daniela González-Zamar & Juan C. Infante-Moro & Germán Ruipérez García, 2020. "Sustainable Management of Digital Transformation in Higher Education: Global Research Trends," Sustainability, MDPI, vol. 12(5), pages 1-24, March.
    6. 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.
    7. Azoulay, Pierre & Stellman, Andrew & Zivin, Joshua Graff, 2006. "PublicationHarvester: An open-source software tool for science policy research," Research Policy, Elsevier, vol. 35(7), pages 970-974, September.
    8. Dongwook Shin & Taehwan Kim & Joongmin Choi & Jungsun Kim, 2014. "Author name disambiguation using a graph model with node splitting and merging based on bibliographic information," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(1), pages 15-50, July.
    9. Giovanni Abramo & Ciriaco Andrea D’Angelo & Fabio Pugini, 2008. "The measurement of Italian universities’ research productivity by a non parametric-bibliometric methodology," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 225-244, August.

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