通过参与度测量社会整合：利用LLMs对 displaced Karelians 采访档案中的组织和休闲活动进行分类
Measuring Social Integration Through Participation: Categorizing Organizations and Leisure Activities in the Displaced Karelians Interview Archive using LLMs
AI总结：
利用LLMs对卡累利亚疏散家庭采访中的组织和休闲活动进行分类，以测量社会整合并生成结构化资源。
摘要：
数字化的历史档案使人们能够大规模研究日常生活中的社会生活，但直接从文本中提取的信息往往无法以定量方式回答历史学家或社会学家提出的问题。我们针对芬兰二战卡累利亚疏散家庭采访的大型集合进行研究。先前的工作从这些采访中提取了超过35万次休闲活动和组织成员的提及，得到了71,000个独特的活动和组织名称——太多无法直接分析。我们开发了一个分类框架，捕捉参与的关键方面（活动/组织的类型、通常的社会性、发生的频率、以及身体需求）。我们注释了一个黄金标准集以允许可靠的评估，然后测试大型语言模型是否能以规模应用相同的模式。通过在多个模型运行中的简单投票方法，我们发现一个开放权重的LLM可以接近专家判断。最后，我们将该方法应用于标记35万实体，产生了一个用于后续研究社会整合及相关结果的结构化资源。
英文摘要：
Digitized historical archives make it possible to study everyday social life on a large scale, but the information extracted directly from text often does not directly allow one to answer the research questions posed by historians or sociologists in a quantitative manner. We address this problem in a large collection of Finnish World War II Karelian evacuee family interviews. Prior work extracted more than 350K mentions of leisure time activities and organizational memberships from these interviews, yielding 71K unique activity and organization names -- far too many to analyze directly. We develop a categorization framework that captures key aspects of participation (the kind of activity/organization, how social it typically is, how regularly it happens, and how physically demanding it is). We annotate a gold-standard set to allow for a reliable evaluation, and then test whether large language models can apply the same schema at scale. Using a simple voting approach across multiple model runs, we find that an open-weight LLM can closely match expert judgments. Finally, we apply the method to label the 350K entities, producing a structured resource for downstream studies of social integration and related outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
