Exploring computational approaches to law: the evolution of judicial language in the Anglo‐Welsh poor law, 1691–1834

Abstract The use of natural language processing (NLP) and machine learning (ML) to analyse the structure of legal texts is a fast‐growing field. While much attention has been devoted to the use of these techniques to predict case outcomes, they have the potential to contribute more broadly to research into the nature of legal reasoning and its relationship to social and economic change. In this article, we use recently developed NLP and ML methods to test the claim that judicial language is systematically shaped by economic shocks deriving from the business cycle and by long‐term trends in the economy associated with technological change and industrial transition. Focusing on cases decided under the Anglo‐Welsh poor law between the 1690s and 1830s, we show that the terminology used to describe the right to poor relief shifted over time according to economic conditions. We explore the implications of our results for the poor law, the theory of legal evolution, and socio‐legal research methods.