用轻量模型自动识别政治文本中的因果关系，提升分析效率
Identifying attributions of causality in political text
- 训练轻量因果语言模型，提取文本中的因果对
- 在少量标注下实现较高准确率，优于人工编码
- 适用于大规模政治文本分析，适合研究者使用
解释是人们理解政治世界的基本方式。公众经常追问事件发生的原因、责任归属以及应如何改进。然而，尽管解释至关重要，其在政治科学中仍缺乏系统性研究，现有方法分散且多局限于特定议题。本文提出一种检测与解析政治文本中解释的框架，通过训练轻量级因果语言模型，将因果陈述结构化为因果对，便于下游分析。实证表明该方法可规模化研究因果解释，具有较低的人工标注需求、良好的泛化能力及与人工标注相当的准确性。
原文摘要 · Abstract (English)
Explanations are a fundamental element of how people make sense of the political world. Citizens routinely ask and answer questions about why events happen, who is responsible, and what could or should be done differently. Yet despite their importance, explanations remain an underdeveloped object of systematic analysis in political science, and existing approaches are fragmented and often issue-specific. I introduce a framework for detecting and parsing explanations in political text. To do this, I train a lightweight causal language model that returns a structured data set of causal claims in the form of cause-effect pairs for downstream analysis. I demonstrate how causal explanations can be studied at scale, and show the method's modest annotation requirements, generalizability, and accuracy relative to human coding.
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