Original
Running With Scissors? Integrating GPT Models Into Public Policy Research
Abstract
ABSTRACT The integration of large language models (LLMs) into public policy research presents both exciting opportunities and methodological challenges. This research note explores how OpenAI's GPT can be used to semi‐automate the annotation of legislative testimony within the Advocacy Coalition Framework, focusing on emotion‐belief dyads. Building on Emotion‐Belief Analysis, we demonstrate how GPT can assist in identifying these complex constructs under human supervision. Our contributions are threefold: (1) we provide practical guidance for applying LLMs to publicly available textual data, (2) we propose a semiautomated workflow that strengthens conceptual clarity, transparency, consistency, replicability, and accessibility, and (3) we reflect on the ethical and methodological implications of LLM‐assisted research. As LLMs continue to advance, this research note aims to help scholars balance innovation with rigor and integrate these tools responsibly into policy research, offering lessons that extend to the study of frames, discourses, narratives, and other ideational dimensions of policymaking.
中文
铤而走险？将GPT模型纳入公共政策研究
摘要
摘要：将大语言模型（LLMs）整合进公共政策研究，既带来令人振奋的机遇，也带来方法论上的挑战。本研究札记探讨如何利用OpenAI的GPT，在倡议联盟框架内对立法证词进行半自动化标注，重点关注情感—信念配对。基于情感—信念分析，我们展示GPT如何在人工监督下协助识别这些复杂构念。我们的贡献有三：第一，为将LLMs应用于公开可得的文本数据提供实践指导；第二，提出一种半自动化工作流程，以增强概念清晰度、透明度、一致性、可复现性和可及性；第三，反思LLM辅助研究的伦理与方法论意涵。随着LLMs不断发展，本札记旨在帮助学者在创新与严谨之间取得平衡，并负责任地将这些工具整合进政策研究，同时提供可推广至框架、话语、叙事以及政策制定其他观念维度的研究的经验教训。
关键词
大语言模型、GPT、公共政策研究、文本标注、倡议联盟框架、情感—信念分析、研究方法、伦理