Construction of a pragmatic base line for journal classifications and maps based on aggregated journal-journal citation relations

• Published in: Journal of informetrics Vol. 10; no. 4; pp. 902 - 918
• Main Authors: Leydesdorff, Loet, Bornmann, Lutz, Zhou, Ping
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.11.2016
• Subjects: Citation; Classification; Disciplines; Subject categories; Subject categories; Disciplines; Journal; Citation; Classification
• ISSN: 1751-1577; 1875-5879
• DOI: 10.1016/j.joi.2016.07.008

[Display omitted] •Using VOSviewer and Pajek, a hierarchical classification of journals is constructed.•Social-science journals form the largest group among the nine top-level fields.•The proposed classification offers also a research and analysis tool.•Journal maps at different levels of granularity enable the user to zoom in and out.•The designation of the clusters is left to the analyst. A number of journal classification systems have been developed in bibliometrics since the launch of the Citation Indices by the Institute of Scientific Information (ISI) in the 1960s. These systems are used to normalize citation counts with respect to field-specific citation patterns. The best known system is the so-called “Web-of-Science Subject Categories” (WCs). In other systems papers are classified by algorithmic solutions. Using the Journal Citation Reports 2014 of the Science Citation Index and the Social Science Citation Index (n of journals=11,149), we examine options for developing a new system based on journal classifications into subject categories using aggregated journal–journal citation data. Combining routines in VOSviewer and Pajek, a tree-like classification is developed. At each level one can generate a map of science for all the journals subsumed under a category. Nine major fields are distinguished at the top level. Further decomposition of the social sciences is pursued for the sake of example with a focus on journals in information science (LIS) and science studies (STS). The new classification system improves on alternative options by avoiding the problem of randomness in each run that has made algorithmic solutions hitherto irreproducible. Limitations of the new system are discussed (e.g. the classification of multi-disciplinary journals). The system’s usefulness for field-normalization in bibliometrics should be explored in future studies.