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This paper introduces a novel pipeline for converting historical sources into structured data by emphasizing action as the fundamental unit of analysis, utilizing the GRAM-framework. By integrating machine learning tools, the authors automate the graphing of actions, allowing for a granular examination of social history while complementing manual graphing techniques. An illustrative case study demonstrates the application of this method by graphing the actions of individuals pretending across various archival collections related to runaways and itinerants in Denmark during the eighteenth and nineteenth centuries.
Automating the graphing of historical actions reveals nuanced social dynamics that traditional methods may overlook.
This working paper describes a pipeline for turning historical sources into structured data organised around the principle of foregrounding action as the basic and constitutive unit of analysis. It is rooted in a desire for pipelines that suit a granular approach to social history. The pipeline rests on the principles developed in the GRAM-framework (Graph of Roles and Actions Model), but leverages a range of machine learning tools to allow for an automated, skeletal graphing of actions. Ideally, such auto-GRAMS would integrate with close readings, including extensive manual graphing. Finally, we provide an example of how this approach might work in practice by graphing actions of pretending across four separate archival collections, relating to runaways and itinerants in eighteenth and nineteenth-century Denmark.
