Abstract
Emerging fields in science and technology are of great interest to innovation researchers, but such fields are often difficult to identify and characterize. This paper outlines a system for identifying a key element of emerging fields: their community of practice, consisting of active scientists and researchers. The system does not simply count these human actors and the interactions between them. Rather, guided by actant network theory, it also examines other non-human actors with which they interact, such as organizations, publications and terminologies. Using quantitative indicators inspired by actant network theory, and derived from features extracted from the full text and metadata of scientific publications and patents, the system attempts to identify communities of practice associated with emerging fields in science and technology. This paper outlines details of these features and indicators, describes how these indicators are combined using Bayesian models, and reports the results of applying these indicators to document sets associated with emerging scientific and technological fields. The results reported in this paper show that system outputs generally agree with subject matter expert judgments with respect to determining the existence of communities of practice, and appear to offer interesting insights into the development of emerging fields.