Thematic analysis and other variants of inductive coding are widely used
qualitative analytic methods within empirical legal studies (ELS). We propose a
novel framework facilitating effective collaboration of a legal expert with a
large language model (LLM) for generating initial codes (phase 2 of thematic
analysis), searching for themes (phase 3), and classifying the data in terms of
the themes (to kick-start phase 4). We employed the framework for an analysis
of a dataset (n=785) of facts descriptions from criminal court opinions
regarding thefts. The goal of the analysis was to discover classes of typical
thefts. Our results show that the LLM, namely OpenAI's GPT-4, generated
reasonable initial codes, and it was capable of improving the quality of the
codes based on expert feedback. They also suggest that the model performed well
in zero-shot classification of facts descriptions in terms of the themes.
Finally, the themes autonomously discovered by the LLM appear to map fairly
well to the themes arrived at by legal experts. These findings can be leveraged
by legal researchers to guide their decisions in integrating LLMs into their
thematic analyses, as well as other inductive coding projects.

使用法律专家与大型语言模型（LLM）的协作框架进行主题分析和归纳编码相关研究，对刑事法院意见中的案件描述进行分析，发现典型盗窃案的类别，并证明 LLM 能有效产生初步编码、提高编码质量以及自动分类案件描述主题，为法律研究人员在整合 LLM 到他们的主题分析项目中提供指导。