Abstract
The accuracy of interdisciplinarity measurements is directly related to the quality of the underlying bibliographic data. Existing indicators of interdisciplinarity are not capable of reflecting the inaccuracies introduced by incorrect and incomplete records because correct and complete bibliographic data can rarely be obtained. This is the case for the Rao–Stirling index, which cannot handle references that are not categorized into disciplinary fields. We introduce a method that addresses this problem. It extends the Rao–Stirling index to acknowledge missing data by calculating its interval of uncertainty using computational optimization. The evaluation of our method indicates that the uncertainty interval is not only useful for estimating the inaccuracy of interdisciplinarity measurements, but it also delivers slightly more accurate aggregated interdisciplinarity measurements than the Rao–Stirling index.Electronic supplementary materialThe online version of this article (doi:10.1007/s11192-016-1842-4) contains supplementary material, which is available to authorized users.
Highlights
Most quantitative measures of the output of InterDisciplinary Research (IDR) rely on bibliometric methods
The evaluation of our method indicates that the uncertainty interval is useful for estimating the inaccuracy of interdisciplinarity measurements, but it delivers slightly more accurate aggregated interdisciplinarity measurements than the Rao–Stirling index
We propose a theoretical extension of the Rao–Stirling index to account for the uncertainty resulting from references that remain uncategorized
Summary
Most quantitative measures of the output of InterDisciplinary Research (IDR) rely on bibliometric methods. An additional problem affects top–down approaches to measure IDR such as the Rao–Stirling diversity index: the need for a predefined taxonomy of disciplines that classifies all publications in the dataset. While bottom– up approaches are suited for capturing emerging developments that do not fit into existing categories, the classification-based approach is useful for large-scale explorations, such as comparisons of areas of science using an extensive amount of data or the disciplinary breadth of research institutions. The latter approach is the focus of this paper
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