Machine learning misclassification networks reveal a citation advantage of interdisciplinary publications only in high-impact journals.

ABSTRACT: Given a large enough volume of data and precise, meaningful categories, training a statistical model to solve a classification problem is straightforward and has become a standard application of machine learning (ML). If the categories are not precise, but rather fuzzy, as in the case of scientific disciplines, the systematic failures of ML classification can be informative about properties of the underlying categories. Here we classify a large volume of academic publications using only the abstract as information. From the publications that are classified differently by journal categories and ML categories (i.e., misclassified publications, when using the journal assignment as ground truth) we construct a network among disciplines. Analysis of these misclassifications provides insight in

SUBMITTER: Lyutov A 

PROVIDER: S-EPMC11412973 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature