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This paper presents a method using LLMs for stance detection in scientific discourse, specifically identifying realism vs. instrumentalism in Bayesian cognitive science articles. The approach combines theory-driven coding, expert annotations, and prompt optimization to achieve high reliability.
This study evaluates the use of open-source LLMs for inductive coding of interviews with Black firearm violence survivors, finding that while LLMs can identify some codes, overall relevance remains low and guardrails cause significant narrative erasure. The research highlights both potential and ethical limitations of applying AI to qualitative research involving vulnerable populations.