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Abstract

We introduce a novel methodology for mapping academic institutions based on their journal publication profiles. We believe that journals in which researchers from academic institutions publish their works can be considered as useful identifiers for representing the relationships between these institutions and establishing comparisons. However, when academic journals are used for research output representation, distinctions must be introduced between them, based on their value as institution descriptors. This leads us to the use of journal weights attached to the institution identifiers. Since a journal in which researchers from a large proportion of institutions published their papers may be a bad indicator of similarity between two academic institutions, it seems reasonable to weight it in accordance with how frequently researchers from different institutions published their papers in this journal. Cluster analysis can then be applied to group the academic institutions, and dendrograms can be provided to illustrate groups of institutions following agglomerative hierarchical clustering. In order to test this methodology, we use a sample of Spanish universities as a case study. We first map the study sample according to an institution's overall research output, then we use it for two scientific fields (Information and Communication Technologies, as well as Medicine and Pharmacology) as a means to demonstrate how our methodology can be applied, not only for analyzing institutions as a whole, but also in different disciplinary contexts.

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Abstract

Google Scholar has been well received by the research community. Its promises of free, universal, and easy access to scientific literature coupled with the perception that it covers the social sciences and the humanities better than other traditional multidisciplinary databases have contributed to the quick expansion of Google Scholar Citations and Google Scholar Metrics: 2 new bibliometric products that offer citation data at the individual level and at journal level. In this article, we show the results of an experiment undertaken to analyze Google Scholar's capacity to detect citation-counting manipulation. For this, we uploaded 6 documents to an institutional web domain that were authored by a fictitious researcher and referenced all the publications of the members of the EC3 research group at the University of Granada. The detection by Google Scholar of these papers caused an outburst in the number of citations included in the Google Scholar Citations profiles of the authors. We discuss the effects of such an outburst and how it could affect the future development of such products, at both the individual level and the journal level, especially if Google Scholar persists with its lack of transparency.

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Date

20. 1.2015 18:30:22