A comparison of the Web of Science and publication-level classification systems of science. Issue 1 (February 2017)

• Record Type: Journal Article
• Authors: Perianes-Rodriguez, Antonio; Ruiz-Castillo, Javier
• Is Part Of: Journal of informetrics. Volume 11:Issue 1(2017:Feb.)
• Page Start: 32
• Page End: 45
• Publication Date: 2017-02
• Subjects: Classification systems of science -- Journal-level versus publication-level systems -- Field-normalization; Library statistics -- Periodicals; Information science -- Statistical methods -- Periodicals; Bibliometrics -- Periodicals; Bibliothèques -- Statistiques -- Périodiques; Sciences de l'information -- Méthodes statistiques -- Périodiques; Bibliométrie -- Périodiques; 020.727
• DOI: 10.1016/j.joi.2016.10.007
• Languages: English
• ISSNs: 1751-1577

Abstract

Highlights: A classification system A is preferable to B when the normalization procedure based on the former performs better than the one based on the latter. Performance is assessed in terms of a graphical and a numerical test that use both classification systems for evaluation purposes. A publication-level algorithmically constructed system of 5, 119 clusters is found to dominate a second one of 1, 363 clusters. Although the system at the highest granularity level and the Web of Science journal-level system are non-comparable, we recommend the former. Abstract: In this paper, we propose a new criterion for choosing between a pair of classification systems of science that assign publications (or journals) to a set of clusters. Consider the standard target (cited-side) normalization procedure in which cluster mean citations are used as normalization factors. We recommend system A over system B whenever the standard normalization procedure based on system A performs better than the standard normalization procedure based on system B. Performance is assessed in terms of two double tests – one graphical, and one numerical – that use both classification systems for evaluation purposes. In addition, a pair of classification systems is compared using a third, independent classification system for evaluation purposes. We illustrate this strategy by comparing a Web of Science journal-level classification system, consisting of 236 journal subject categories, with two publication-level algorithmically constructed classification systems consisting of 1363 and 5119 clusters. There are two main findings. Firstly, the second publication-level system is found to dominate the first. Secondly, the publication-level system at the highest granularity level and the Web of Science journal-level system are found to be non-comparable. Nevertheless, we find reasons to recommend the publication-level option.