Investigating patterns of change, stability, and interaction among scientific disciplines using embeddings

Abstract

Multi-disciplinary and inter-disciplinary collaboration can be an appropriate response to tackling the increasingly complex problems faced by today’s society. Scientific disciplines are not rigidly defined entities and their profiles change over time. No previous study has investigated multiple disciplinarity (i.e. the complex interaction between disciplines, whether of a multidisciplinary or an interdisciplinary nature) at scale with quantitative methods, and the change in the profile of disciplines over time. This article explores a dataset of over 21 million articles published in 8400 academic journals between 1990 and 2019 and proposes a new scalable data-driven approach to multiple disciplinarity. This approach can be used to study the relationship between disciplines over time. By creating vector representations (embeddings) of disciplines and measuring the geometric closeness between the embeddings, the analysis shows that the similarity between disciplines has increased over time, but overall the size of their neighbourhood (the number of neighbouring disciplines) has decreased, pointing to disciplines being more similar to each other over time, while at the same time displaying increased specialisation. We interpret this as a pattern of global convergence combined with local specialisation. Our approach is also able to track the development of disciplines’ profiles over time, detecting those that changed the most in the time period considered, and to treat disciplines as compositional units, where relationships can be expressed as analogy equations of the form Discipline1 + Discipline2 ≈ Discipline3. These findings can help researchers, academic institutions and organizations to better understand and react to the dynamics of scientific research, and can support the education sector in designing curricula or in the recruitment of academics and researchers.