In this paper, we assess cross disciplinary of researc h produced by the Italian Academic Statisticians and by Social Statisticians combining text mining and bibliometrics techniques. Interdisciplinary research would be supported and encouraged to solve new statistical challenges. The term interdisciplinary tends to be tacitly understood by researchers, without shared definition. We adopt the definition suggested by Porter et al. (2007) interdisciplinary research requires an integration of concepts, theories, techniques and/or data from two or more bodies of sp ecialized knowledge. Multidisciplinary research may incorporate elements of other specialized knowledges, but without interdisciplinary synthesis (Wagner et al., 2011) which includes more than single parts. Analysis of cross disciplinary improves traditional indicators assessing and quantifying interdisciplinary research. Indicators of different disciplinary describe heterogeneity of a bibliometric set obtained starting from predefined categories. Network coherence indicators are constructed to measure the intensity of similarity relations within a bibliometric set using a bottom-up approach, which reveals the structural consistency of the publications network (Rafols and Meyer, 2010). We incorporate both points of view and show how to improve on existing text-based and bibliometric methods. We propose an hybrid clustering procedure based on Fisher’s inverse chi-square method for integrating textual content and citation information. Given clustered papers, it’s possible to evaluate ISI subject categories as descriptive labels for statistical documents, and to address individual researchers interdiscip linary based on monthly Italian Statisticians papers, for ten years from 2001 to 2011. Also the data gathered on the research of Social Statisticians will be analyzed.

Assessing Italian Research in Social Statistics, 2013.

Assessing Italian Research in Social Statistics

Zavarrone, Emma
2013-01-01

Abstract

In this paper, we assess cross disciplinary of researc h produced by the Italian Academic Statisticians and by Social Statisticians combining text mining and bibliometrics techniques. Interdisciplinary research would be supported and encouraged to solve new statistical challenges. The term interdisciplinary tends to be tacitly understood by researchers, without shared definition. We adopt the definition suggested by Porter et al. (2007) interdisciplinary research requires an integration of concepts, theories, techniques and/or data from two or more bodies of sp ecialized knowledge. Multidisciplinary research may incorporate elements of other specialized knowledges, but without interdisciplinary synthesis (Wagner et al., 2011) which includes more than single parts. Analysis of cross disciplinary improves traditional indicators assessing and quantifying interdisciplinary research. Indicators of different disciplinary describe heterogeneity of a bibliometric set obtained starting from predefined categories. Network coherence indicators are constructed to measure the intensity of similarity relations within a bibliometric set using a bottom-up approach, which reveals the structural consistency of the publications network (Rafols and Meyer, 2010). We incorporate both points of view and show how to improve on existing text-based and bibliometric methods. We propose an hybrid clustering procedure based on Fisher’s inverse chi-square method for integrating textual content and citation information. Given clustered papers, it’s possible to evaluate ISI subject categories as descriptive labels for statistical documents, and to address individual researchers interdiscip linary based on monthly Italian Statisticians papers, for ten years from 2001 to 2011. Also the data gathered on the research of Social Statisticians will be analyzed.
2013
Assessing Italian Research in Social Statistics, 2013.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10808/11505
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