A comparison of the Web of Science and publication-level classification systems of science

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. (C) 2016 Elsevier Ltd. All rights reserved.This research project builds on earlier work started by Antonio Perianes- Rodriguez during a research visit to the Centre for Science and Technology Studies (CWTS) of Leiden University as awardee of José Castillejo grant, CAS15/00178, funded by the Spanish MEC. Ruiz- Castillo is a visiting researcher at CWTS and gratefully acknowledges CWTS for the use of its data. Ruiz-Castillo acknowledges financial support from the Spanish MEC through grant ECO2014-55953- P, as well as grant MDM 2014-0431 to his Departamento de Economía