We aim to explore the relationship between legal evolution and industrialisation by using computational approaches to the analysis of legal texts. The project will help answer some unresolved questions concerning the nature and direction of legal change during the British industrial revolution, while also advancing the use of statistical methods to study judicial decision making.

Using natural language processing (NLP) and machine learning (ML) approaches to analyse the structure of legal texts is a small but fast growing field. While much attention has been devoted to the use of these techniques to predict case outcomes under current laws, our approach is different: using historical data, we aim to see how far changes in the content and structure of judicial decisions over time are driven by wider economic factors. Specifically, we take advantage of recently developed NLP and ML methods to test the claim that judges decide cases not solely according to established rules (the doctrine of precedent) but in ways that are systematically shaped by economic shocks, the business cycle, and secular trends associated with technological change and long-run industrial transitions.