Machine-Learning Human Rights

Utilizing a comprehensive panel dataset spanning from 1900 to 2020, this study introduces an innovative methodology for the analysis and categorization of legal documents, specifically national constitutions. Contrary to the predominant reliance on unsupervised methods within the field, this research incorporates a supervised machine-learning approach, notably the SEMMS method, alongside traditional unsupervised algorithms. This dual approach facilitates a nuanced analysis of the human rights provisions contained within national constitutions, resulting in the identification of both traditional and novel constitutional groupings. Broadly speaking, the more traditional common law-civil law divide does not seem particularly relevant in this context. Furthermore, our methodology enables the examination of “switchers”—nations transitioning between groupings—thereby shedding light on critical moments of constitutional reclassification. By pinpointing the key variables that delineate these groupings and transitions, our findings not only complement previous scholarly insights but also unveil unique patterns of constitutional evolution. The implications of our research extend beyond constitutional studies, offering valuable insights and methodological advancements for the analysis of extensive legal corpora across various domains.