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Scientometrics
Authors: Stephen Carley and Alan L. Porter

to measure the “integration” of academic disciplines. These analyses seek to assess how an author or publication weaves previously disparate knowledge into one fabric. The “Integration score” calculates the diversity of a set of references based upon

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Introduction In recent years, a number of measures have been proposed to formalize the notion of diversity in the context of research evaluation. A very promising direction of this investigation was the research experimenting

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Introduction ‘Diversity’ is a concept that features prominently in a variety of disparate disciplines (Stirling 2007 ). Yet it is almost impossible to find a precise definition. In the nineties, Stirling ( 1998 ), following

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fine-grained classification information in the patent document provides a valuable indicator of the technological breadth and diversity of the patent (Nerka and Shane 2007 ). At an aggregated level, we may observe the diverse capacity and relative

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diversity of classifications assigned to backward citations (patents cited by the subject patent) and the number of forward citations (patents citing the subject patent) by focusing on the classifications that represent the field of patented technology and

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has his or her own specific expertise. Diversity among members of collaborative research teams might thus serve as an extra source to reinforce research quality. Theoretical background Social capital and

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Scientometrics
Authors: Isabel Gómez, Maria Bordons, M. Fernández, and Aida Méndez

Abstract  

The delimitation of a research field in bibliometric studies presents the problem of the diversity of subject classifications used in the sources of input and output data. Classification of documents according to thematic codes or keywords is the most accurate method, mainly used in specialised bibliographic or patent databases. Classification of journals in disciplines presents lower specificity, and some shortcomings as the change over time of both journals and disciplines and the increasing interdisciplinarity of research. Differences in the criteria in which input and output data classifications are based obliges to aggregate data in order to match them. Standardization of subject classifications emerges as an important point in bibliometric studies in order to allow international comparisons, although flexibility is needed to meet the needs of local studies.

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Abstract  

The multidimensional character and inherent conflict with categorisation of interdisciplinarity makes its mapping and evaluation a challenging task. We propose a conceptual framework that aims to capture interdisciplinarity in the wider sense of knowledge integration, by exploring the concepts of diversity and coherence. Disciplinary diversity indicators are developed to describe the heterogeneity of a bibliometric set viewed from predefined categories, i.e. using a top-down approach that locates the set on the global map of science. Network coherence indicators are constructed to measure the intensity of similarity relations within a bibliometric set, i.e. using a bottom-up approach, which reveals the structural consistency of the publications network. We carry out case studies on individual articles in bionanoscience to illustrate how these two perspectives identify different aspects of interdisciplinarity: disciplinary diversity indicates the large-scale breadth of the knowledge base of a publication; network coherence reflects the novelty of its knowledge integration. We suggest that the combination of these two approaches may be useful for comparative studies of emergent scientific and technological fields, where new and controversial categorisations are accompanied by equally contested claims of novelty and interdisciplinarity.

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Abstract  

We analyze the relation between funding and output using bibliometric methods with field normalized data. Our approach is to connect individual researcher data on funding from Swedish university databases to data on incoming grants using the specific personal ID-number. Data on funding include the person responsible for the grant. All types of research income are considered in the analysis yielding a project database with a high level of precision. Results show that productivity can be explained by background variables, but that quality of research is more or less un-related to background variables.

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