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Partition-based Field Normalization: An approach to highly specialized publication records

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  • Rons, Nadine

Abstract

Field normalized citation rates are well-established indicators for research performance from the broadest aggregation levels such as countries, down to institutes and research teams. When applied to still more specialized publication sets at the level of individual scientists, also a more accurate delimitation is required of the reference domain that provides the expectations to which a performance is compared. This necessity for sharper accuracy challenges standard methodology based on pre-defined subject categories. This paper proposes a way to define a reference domain that is more strongly delimited than in standard methodology, by building it up out of cells of the partition created by the pre-defined subject categories and their intersections. This partition approach can be applied to different existing field normalization variants. The resulting reference domain lies between those generated by standard field normalization and journal normalization. Examples based on fictive and real publication records illustrate how the potential impact on results can exceed or be smaller than the effect of other currently debated normalization variants, depending on the case studied. The proposed Partition-based Field Normalization is expected to offer advantages in particular at the level of individual scientists and other very specific publication records, such as publication output from interdisciplinary research.

Suggested Citation

  • Rons, Nadine, 2012. "Partition-based Field Normalization: An approach to highly specialized publication records," Journal of Informetrics, Elsevier, vol. 6(1), pages 1-10.
  • Handle: RePEc:eee:infome:v:6:y:2012:i:1:p:1-10
    DOI: 10.1016/j.joi.2011.09.008
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    References listed on IDEAS

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    Cited by:

    1. Robin Haunschild & Lutz Bornmann, 2018. "Field- and time-normalization of data with many zeros: an empirical analysis using citation and Twitter data," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 997-1012, August.
    2. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    3. Bornmann, Lutz & Haunschild, Robin, 2018. "Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data," Journal of Informetrics, Elsevier, vol. 12(3), pages 998-1011.
    4. Ruiz-Castillo, Javier & Waltman, Ludo, 2015. "Field-normalized citation impact indicators using algorithmically constructed classification systems of science," Journal of Informetrics, Elsevier, vol. 9(1), pages 102-117.
    5. Bornmann, Lutz & Haunschild, Robin, 2016. "Citation score normalized by cited references (CSNCR): The introduction of a new citation impact indicator," Journal of Informetrics, Elsevier, vol. 10(3), pages 875-887.
    6. Bornmann, Lutz & Haunschild, Robin & Adams, Jonathan, 2019. "Do altmetrics assess societal impact in a comparable way to case studies? An empirical test of the convergent validity of altmetrics based on data from the UK research excellence framework (REF)," Journal of Informetrics, Elsevier, vol. 13(1), pages 325-340.
    7. Franceschini, Fiorenzo & Maisano, Domenico, 2014. "Sub-field normalization of the IEEE scientific journals based on their connection with Technical Societies," Journal of Informetrics, Elsevier, vol. 8(3), pages 508-533.
    8. Shir Aviv-Reuven & Ariel Rosenfeld, 2023. "A logical set theory approach to journal subject classification analysis: intra-system irregularities and inter-system discrepancies in Web of Science and Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 157-175, January.
    9. Rons, Nadine, 2018. "Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references," Journal of Informetrics, Elsevier, vol. 12(1), pages 113-132.

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