LLM-Assisted Stance Detection in Scientific Discourse: A Test Case in Bayesian Cognitive Science
arXiv:2606.15566v1 Announce Type: new Abstract: Qualitative coding is central to social science, but expert annotation is difficult to scale. LLMs offer a possible extension, yet require careful validation when the target construct is interpretive, theoretically loaded, and only indirectly expressed. We study this problem in a difficult case: detecting whether authors treat Bayesian models as descriptions of mental and neural mechanisms (realism) or as useful mathematical tools (instrumentalism). Our method combines a theory-driven codebook, expert-coded reference annotations, a diagnostic-gat
The proliferation of advanced LLMs is creating new opportunities and challenges for scaling expert-level qualitative research, making this validation research timely.
This development indicates concrete progress in leveraging AI for complex interpretive tasks, potentially transforming qualitative research methodologies across various scientific fields.
The ability to reliably automate or assist in nuanced qualitative coding shifts the resource allocation for social science research, making previously intractable projects feasible.
- · Social Science Researchers
- · AI-powered Research Platforms
- · Qualitative Data Analysis Software
- · Cognitive Science
- · Traditional Manual Coding Services
- · Research relying solely on small-scale qualitative studies
LLMs can assist human experts in scaling qualitative coding efforts for complex, theoretical constructs.
This could lead to a significant increase in the volume and scope of qualitative research studies across social sciences.
New research questions become approachable, potentially accelerating theory development in fields requiring deep textual analysis.
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Read at arXiv cs.CL