Assessing Italian Research in Statistics:Interdisciplinary or Multidisciplinary?

In this paper, we assess cross disciplinary of research produced by the Italian Academic Statisticians (IAS) combining text mining and bibliometrics techniques Textual and bibliometric approaches have together advantages and disadvantages, and provide different views on the same interlinked corpus of scientific publications. In addition textual information in such documents, jointly citations also constitute huge networks that yield additional information. We incorporate both points of view and show how to improve on existing text-based and bibliometric methods. In particular, we propose an hybrid clustering procedure based on Fisher ╒s inverse chi-square method as the preferred method for integrating textual content and citation information. Given clustered papers, it’s possible to evaluate ISI subject categories (SCs) as descriptive labels for statistical documents, and to address individual researchers interdisciplinary.