将历史文献转化为以行动为核心的结构化数据，助力微观社会史研究。
Granularity in Action: Graphing sources for social history
- 以行动为基本单元，构建自动化历史文本图谱
- 基于GRAM框架实现跨档案的行动关系抽取
- 适合关注微观历史与数字人文的研究者
本文提出一种将历史文献转化为结构化数据的流程，核心是将行动作为社会史分析的基本单位。该流程基于GRAM框架（角色与行动图模型），结合多种机器学习工具，实现对历史文本中行动的自动化初步图谱构建。理想情况下，自动生成的GRAM可与深度人工校对和细读相结合。文章以18至19世纪丹麦四组档案中的“假装”行为为例，展示了该方法在逃亡者与流浪者研究中的实际应用。
原文摘要 · Abstract (English)
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.
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