Quantifying the rise and fall of scientific fields

TL;DR

This paper develops a scale-invariant method to quantify and compare the rise and fall patterns of scientific fields, revealing common evolutionary stages and team dynamics across diverse disciplines using arXiv preprints.

Contribution

It introduces a novel approach to identify universal patterns in scientific field evolution and characterizes the changing roles of interdisciplinary and specialized teams over time.

Findings

Fields follow a rise and fall pattern modeled by a Gumbel distribution.

Early phases involve interdisciplinary small teams; later phases involve large specialized teams.

The method enables quantitative comparison of research field dynamics.

Abstract

Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas is reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying scale-invariant patterns in the evolution of scientific fields, and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follows a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific rescaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by the mixing of cognitively distant fields by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies.