Replication Data for "Applications of GPT in Political Science Research: Extracting Information from Unstructured Text"

This paper explores the use of large language models (LLMs), specifically GPT, for
enhancing information extraction from unstructured text in political science research.
By automating the retrieval of explicit details from sources such as historical
documents, meeting minutes, news articles, and unstructured search results, GPT
significantly reduces the time and resources required for data collection. The study
highlights how GPT complements human research assistants, combining automated
efficiency with human oversight to improve the reliability and depth of research. This
integration not only makes comprehensive data collection more accessible, but also
increases the overall research efficiency and scope of research. The paper highlights
GPT’s unique capabilities in information extraction and its potential to advance
empirical research in the field. Additionally, we discuss ethical concerns related to
student employment, privacy, bias, and environmental impact associated with the use
of LLMs.