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.