Granularity of algorithmically constructed publication-level classifications of research publications: Identification of specialties
In this work, we build on and use the outcome of an earlier study on topic identification in an algorithmically constructed publication-level classification (ACPLC), and address the issue of how to algorithmically obtain a classification of topics (containing articles), where the classes of the classification correspond to specialties. The methodology we propose, which is similar to that used in the earlier study, uses journals and their articles to construct a baseline classification. The underlying assumption of our approach is that journals of a particular size and focus have a scope that corresponds to specialties. By measuring the similarity between (1) the baseline classification and (2) multiple classifications obtained by topic clustering and using different values of a resolution parameter, we have identified a best performing ACPLC. In two case studies, we could identify the subject foci of the specialties involved, and the subject foci of specialties were relatively easy to distinguish. Further, the class size variation regarding the best performing ACPLC is moderate, and only a small proportion of the articles belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable for determining the specialty granularity level of an ACPLC.