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Date

22. 3.2009 19:22:35

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Abstract

Journal articles constitute the core documents for the diffusion of knowledge in the natural sciences. It has been argued that the same is not true for the social sciences and humanities where knowledge is more often disseminated in monographs that are not indexed in the journal-based databases used for bibliometric analysis. Previous studies have made only partial assessments of the role played by both serials and other types of literature. The importance of journal literature in the various scientific fields has therefore not been systematically characterized. The authors address this issue by providing a systematic measurement of the role played by journal literature in the building of knowledge in both the natural sciences and engineering and the social sciences and humanities. Using citation data from the CD-ROM versions of the Science Citation Index (SCI), Social Science Citation Index (SSCI), and Arts and Humanities Citation Index (AHCI) databases from 1981 to 2000 (Thomson ISI, Philadelphia, PA), the authors quantify the share of citations to both serials and other types of literature. Variations in time and between fields are also analyzed. The results show that journal literature is increasingly important in the natural and social sciences, but that its role in the humanities is stagnant and has even tended to diminish slightly in the 1990s. Journal literature accounts for less than 50% of the citations in several disciplines of the social sciences and humanities; hence, special care should be used when using bibliometric indicators that rely only on journal literature.

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Abstract

We use a technique recently developed by V. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre (2008) to detect scientific specialties from author cocitation networks. This algorithm has distinct advantages over most previous methods used to obtain cocitation clusters since it avoids the use of similarity measures, relies entirely on the topology of the weighted network, and can be applied to relatively large networks. Most importantly, it requires no subjective interpretation of the cocitation data or of the communities found. Using two examples, we show that the resulting specialties are the smallest coherent groups of researchers (within a hierarchy of cluster sizes) and can thus be identified unambiguously. Furthermore, we confirm that these communities are indeed representative of what we know about the structure of a given scientific discipline and that as specialties, they can be accurately characterized by a few keywords (from the publication titles). We argue that this robust and efficient algorithm is particularly well-suited to cocitation networks and that the results generated can be of great use to researchers studying various facets of the structure and evolution of science.