Scaling social science research

OpenAI has introduced GABRIEL, an open-source toolkit that leverages GPT models to convert qualitative data—such as text and images—into structured quantitative data for social science research. Traditionally, analyzing open-ended survey responses, interview transcripts, or visual materials requires labor-intensive manual coding. GABRIEL automates this process, enabling researchers to scale their analyses without sacrificing nuance. The toolkit is built on GPT and can handle diverse data types, generating numeric variables that feed into statistical models. By releasing GABRIEL as open source, OpenAI invites the research community to adapt and extend its capabilities. For developers building AI workflows, this demonstrates a practical pattern: using large language models to extract structured information from unstructured sources. While designed for social science, the underlying approach applies to any domain where raw text or images need systematic categorization—customer feedback, content moderation, or historical archives. The toolkit is available on GitHub and requires an OpenAI API key. Although still early-stage, it highlights a growing trend of pairing LLMs with rigorous methodological frameworks.