Why GATOS Matters Qualitative data are often where social scientists go when numbers are not enough. Open-ended survey responses, interviews, and written reflections can capture forms of meaning that are difficult to reduce to numbers. As Saldaña (2011) argues, qualitative data analysis is fundamentally a process of meaning-making: researchers construct patterns, identify relationships, and move between inductive and deductive forms of reasoning. The difficulty is that this

Paradigm Shift from Traditional Coding to Conversational Analysis The rapid emergence of generative artificial intelligence (genAI) tools is fundamentally reshaping how researchers engage with unstructured data, particularly within the domain of qualitative analysis. While these technological advancements offer exciting possibilities, they also bring significant challenges. Traditional qualitative coding methods, though rigorous, are often extremely time-intensive, whereas ge

AI in Qualitative Research: Collaborative Partner or Epistemic Risk? The rise of Generative Artificial Intelligence (GenAI) has sparked a profound scholarly debate in the field of qualitative research, centered on a critical question: Do Large Language Models (LLMs) enhance the depth of qualitative exploration, or do they fundamentally threaten its epistemological foundations? Adam S. Hayes (2025) argues that LLMs are revolutionizing how scholars work with textual data by all

Unpacking Bias in AI in Qualitative Research GenAI Is More Than a Tool Looking back from the mid-2020s, it is hard to think of any recent change in academia more disruptive than the rise of generative artificial intelligence (GenAI). As Deleuze and Guattari wrote in the 1980s, “Tools exist only in relation to the interminglings they make possible or that make them possible.” Today, this observation feels especially relevant to new AI technologies, and particularly to GenAI. G