Calatrava Moreno María Del Carmen, Auzinger Thomas, Werthner Hannes
E-Commerce Group, Institute of Software Technology and Interactive Systems, Vienna University of Technology, Vienna, Austria.
Computer Graphics Group, Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria ; IST Austria, Klosterneuburg, Austria.
Scientometrics. 2016;107:213-232. doi: 10.1007/s11192-016-1842-4. Epub 2016 Feb 9.
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.
跨学科性测量的准确性与基础书目数据的质量直接相关。现有的跨学科性指标无法反映因记录不正确和不完整而引入的不准确之处,因为很少能获得正确且完整的书目数据。饶 - 斯特林指数就是这种情况,它无法处理未归类到学科领域的参考文献。我们引入了一种解决此问题的方法。它扩展了饶 - 斯特林指数,通过使用计算优化来计算其不确定性区间,以承认数据缺失的情况。对我们方法的评估表明,不确定性区间不仅有助于估计跨学科性测量的不准确程度,而且与饶 - 斯特林指数相比,它还能提供略微更准确的跨学科性综合测量结果。