Applying Machine Learning to Research on Historical Archives

The paper investigates how machine learning can be applied to analyse large volumes of archival texts, opening new avenues for systematic, interdisciplinary study of historical political phenomena. It examines the intersections of political theory, history, and indigenous studies through 17th- and 18th-century Spanish-language manuscripts from Spain’s Archivo General de Indias, focusing on archival data about the Lacandon People of Central America (Today's Guatemala and Mexico) to inform debates on the legacies of imperialism. Situated within ongoing debates about the relationship between political theory and history, the research explores how historical data can help rethink that relationship. By combining historical inquiry with contemporary computational methods, the study highlights the potential of interdisciplinary approaches to deepen our understanding of enduring effects of political events.