Partitioning highly, medium and lowly cited publications

Abstract

Dividing papers based on their numbers of citations into several groups constitutes one of the most common research practices in bibliometrics and beyond. However, existing dividing methods are both arbitrary and subject to bias. This article proposes a novel approach to partition highly, medium and lowly cited publications based on their citation distribution. We utilise the whole Web of Science (WoS) dataset to demonstrate how to apply this approach to scholarly datasets and examine the robustness of our algorithm in each of the six disciplines under the WoS dataset. The codes that underlie the algorithm are available online.