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Scientometrics
Authors:
Frizo Janssens
,
Wolfgang Glänzel
, and
Bart De Moor

Abstract  

Previous studies have shown that hybrid clustering methods that incorporate textual content and bibliometric information can outperform clustering methods that use only one of these components. In this paper we apply a hybrid clustering method based on Fisher’s inverse chisquare to integrate full-text with citations and to provide a mapping of the field of information science. We quantitatively and qualitatively asses the added value of such an integrated analysis and we investigate whether the clustering outcome is a better representation of the field by comparing with a text-only clustering and with another hybrid method based on linear combination of distance matrices. Our data set consists of almost 1000 articles and notes published in the period 2002–2004 in 5 representative journals. The optimal number of clusters for the field is 5, determined by using a combination of distance-based and stability-based methods. Term networks present the cognitive structure of the field and are complemented by the most representative publications. Three large traditional sub-disciplines, particularly, information retrieval, bibliometrics/scientometrics, and more social aspects, and two smaller clusters about patent analysis and webometrics, can be distinguished.

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Abstract  

A novel subject-delineation strategy has been developed for the retrieval of the core literature in bioinformatics. The strategy combines textual components with bibliometric, citation-based techniques. This bibliometrics-aided search strategy is applied to the 1980–2004 annual volumes of the Web of Science. Retrieved literature has undergone a structural as well as quantitative analysis. Patterns of national publication activity, citation impact and international collaboration are analysed for the 1990s and the new millennium.

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Abstract  

The objective of this study is to use a clustering algorithm based on journal cross-citation to validate and to improve the journal-based subject classification schemes. The cognitive structure based on the clustering is visualized by the journal cross-citation network and three kinds of representative journals in each cluster among the communication network have been detected and analyzed. As an existing reference system the 15-field subject classification by Glänzel and Schubert (Scientometrics 56:55–73, <cite>2003</cite>) has been compared with the clustering structure.

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Abstract  

In this paper we examine whether and to what extent material transfer agreements influence research agenda setting in biotechnology. Research agendas are mapped through patents, articles, letters, reviews, and notes. Three groups are sampled: (1) documents published by government and industry which used research materials received through those agreements, (2) documents published by government and industry which used in-house materials, (3) documents published by academia. Methodologically, a co-word analysis is performed to detect if there is a difference in underlying scientific structure between the first two groups of documents. Secondly, interviews with practitioners of industry and government are intended to capture their opinion regarding the impact of the signed agreements on their own research agenda choices. The existence of synchronic and diachronic common terms between co-word clusters, stemming from the first two groups of publications, suggests cognitive linkage. Moreover, interviewees generally do not consider themselves constrained in research agenda setting when signing agreements for receiving research materials. Finally, after applying a co-word analysis to detect if the first group of documents overlaps with the third group we cannot conclude that agreements signed by industry and government affect research agenda setting in academia.

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