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The aggregated journal-journal citation matrix -based on the Journal CitationReports (JCR) of the Science Citation Index- can be decomposed by indexersand/or algorithmically. In this study, we test the results of two recentlyavailable algorithms for the decomposition of large matrices against twocontent-based classifications of journals: the ISI Subject Categories and thefield/subfield classification of Glaenzel & Schubert (2003). The content-basedschemes allow for the attribution of more than a single category to a journal,whereas the algorithms maximize the ratio of within-category citations overbetween-category citations in the aggregated category-category citation matrix.By adding categories, indexers generate between-category citations, which mayenrich the database, for example, in the case of inter-disciplinarydevelopments. The consequent indexer effects are significant in sparse areas ofthe matrix more than in denser ones. Algorithmic decompositions, on the otherhand, are more heavily skewed towards a relatively small number of categories,while this is deliberately counter-acted upon in the case of content-basedclassifications. Because of the indexer effects, science policy studies and thesociology of science should be careful when using content-basedclassifications, which are made for bibliographic disclosure, and not for thepurpose of analyzing latent structures in scientific communications. Despitethe large differences among them, the four classification schemes enable us togenerate surprisingly similar maps of science at the global level. Erroneousclassifications are cancelled as noise at the aggregate level, but may disturbthe evaluation locally.

Content-based and Algorithmic Classifications of Journals: Perspectives on the Dynamics of Scientific Communication and Indexer Effects