Peer-Reviewed Research
Our analytical approach is documented in peer‑reviewed research across Nature and SAGE journals. These papers validate the GATOS workflow and its application to real‑world qualitative datasets.
Katz, A.
Introduces the GATOS workflow — a systematic approach to AI-assisted thematic analysis that preserves full traceability from source utterances through extracts, clusters, and codes to final themes. Demonstrates how constrained code generation prevents hallucination while maintaining methodological rigor.
Anakok, I., Katz, A., Chew, K.J., & Matusovich, H.
Explores the alignment between NLP/generative AI techniques and traditional thematic analysis phases. Demonstrates the workflow on a real-world dataset and highlights the central role of researchers in AI-assisted qualitative analysis.
Katz, A., Gerhardt, M., & Soledad, M.
Presents a novel method for analyzing large-scale qualitative datasets using NLP and LLMs. Applied to a corpus of 5,000 student evaluations, the method extracts, embeds, clusters, and summarizes responses to generate defensible codebooks.
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