The ISI subject categories classify journals included in the Science Citation Index (SCI). The aggregated journal-journal citation matrix contained in the Journal Citation Reports can be aggregated on the basis of these categories. This leads to an asymmetrical transaction matrix (citing versus cited) which is much more densely populated than the underlying matrix at the journal level. Exploratory factor analysis leads us to opt for a fourteen-factor solution. This solution can easily be interpreted as the disciplinary structure of science. The nested maps of science (corresponding to 14 factors, 172 categories, and 6,164 journals) are brought online at

In addition to science citation indicators of journals like impact and immediacy, social network analysis provides a set of centrality measures like degree, betweenness, and closeness centrality. These measures are first analyzed for the entire set of 7,379 journals included in the Journal Citation Reports of the Science Citation Index and the Social Sciences Citation Index 2004, and then also in relation to local citation environments which can be considered as proxies of specialties and disciplines. Betweenness centrality is shown to be an indicator of the interdisciplinarity of journals, but only in local citation environments and after normalization because otherwise the influence of degree centrality (size) overshadows the betweenness-centrality measure. The indicator is applied to a variety of citation environments, including policy-relevant ones like biotechnology and nanotechnology. The values of the indicator remain sensitive to the delineations of the set because of the indicator’s local character. Maps showing 1 interdisciplinarity of journals in terms of betweenness centrality can be drawn using information about journal citation environments which is available online.

The aggregated journal-journal citation matrix—based on the Journal Citation Reports (JCR) of the Science Citation Index—can be decomposed by indexers and/or algorithmically. In this study, we test the results of two recently available algorithms for the decomposition of large matrices against two content-based classifications of journals: the ISI Subject Categories and the field/subfield classification of Glänzel & Schubert (2003). The content-based schemes allow for the attribution of more than a single category to a journal, whereas the algorithms maximize the ratio of within-category citations over between-category citations in the aggregated category-category citation matrix. By adding categories, indexers generate between-category citations, which may enrich the database, for example, in the case of inter-disciplinary developments. The consequent indexer effects are significant in sparse areas of the matrix more than in denser ones. Algorithmic decompositions, on the other hand, are more heavily skewed towards a relatively small number of categories, while this is deliberately counter-acted upon in the case of content-based classifications. Because of the indexer effects, science policy studies and the sociology of science should be careful when using content-based classifications, which are made for bibliographic disclosure, and not for the purpose of analyzing latent structures in scientific communications. Despite the large differences among them, the four classification schemes enable us to generate surprisingly similar maps of science at the global level. Erroneous classifications are cancelled as noise at the aggregate level, but may disturb the evaluation locally.

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Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. We discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices and Scopus, on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI-databases and with Scopus. Pajek enables us to combine the visualization with statistical analysis, whereas the Google Maps and its derivatives provide superior tools at the Internet.

This study explores the analysis of citations in the Journal of Consumer Research (JCR) during its first 15 years of publication. We review previous work on citation analysis in marketing and consumer research, and we argue for the value of a more complex approach based on patterns of cocitation. Toward this end, we develop a data base that draws on the work of the 42 most frequently published authors in JCR in the first 15 years. We introduce a new, two-stage procedure to investigate the underlying structure in the from-versus-to or citing-cited matrix based on numbers of references among these authors. Our procedure yields a scale of citing-cited asymmetry for the 42 consumer researchers, a "citation-similarity space " showing patterns of symmetric citation among the researchers, and measures of research atypicality. These separate yet complementary results give interesting insights into patterns of cocitation among consumer behavior researchers and thereby appear to reflect the intellectual structure of consumer research. T he development, dissemination, and utilization of knowledge in any academic field of inquiry de-pends on the circulation of ideas through the publica-

We propose an approach to visualizing the scientific world and its evolution by constructing minimum spanning trees (MSTs) and a two-dimensional map of scientific journals using the database of the Science Citation Index (SCI) during 1994–2001. The structures of constructed MSTs are consistent with the sorting of SCI categories. The map of science is constructed based on our MST results. Such a map shows the relation among various knowledge clusters and their citation properties. The temporal evolution of the scientific world can also be delineated in the map. In particular, this map clearly shows a linear structure of the scientific world, which contains three major domains including physical sciences, life sciences, and medical sciences. The interaction of various knowledge fields can be clearly seen from this scientific world map. This approach can be applied to various levels of knowledge domains.