Machine learning misclassification of academic publications reveals non-trivial interdependencies of scientific disciplines (Nov, 10.1007/s11192-020-03789-8, 2020)

Exploring the production of knowledge with quantitative methods is the foundation of scientometrics. In an application of machine learning to scientometrics, we here consider the classification problem of the mapping of academic publications to the subcategories of a multidisciplinary journal—and hence to scientific disciplines—based on the information contained in the abstract. In contrast to standard classification tasks, we are not interested in maximizing the accuracy, but rather we ask, whether the failures of an automatic classification are systematic and contain information about the system under investigation. These failures can be represented as a ’misclassification network’ inter-relating scientific disciplines. Here we show that this misclassification network (1) gives a markedly different pattern of interdependencies among scientific disciplines than common ’maps of science’, (2) reveals a statistical association between misclassification and citation frequencies, and (3) allows disciplines to be classified as ’method lenders’ and ’content explorers’, based on their in-degree out-degree asymmetry. On a more general level, in a wide range of machine learning applications misclassification networks have the potential of extracting systemic information from the failed classifications, thus allowing to visualize and quantitatively assess those aspects of a complex system, which are not machine learnable. © 2021, The Author(s).