LLM-Assisted Grey-Literature Theory Building

Agarwal et al.'s secondary contribution (arXiv 2607.07980): a scalable template for constructing grounded theory from thousands of practitioner documents instead of a few dozen interviews — LLMs do the mechanical, quote-anchored open coding (38,709 docs collected → Gemini relevance judge at κ=0.75 → 3,100 coded with the multi-agent Thematic-LM under three deliberately-polarized coder lenses → 4,838 codes / 109,951 quotes at ~$0.35/doc) while humans keep the interpretive axial/selective coding; automating that back half FAILED (a bottom-up pass yielded 15,029 shallow, redundant causal statements), so the codes→theory step stayed a manual, LLM-as-search-engine process — a division-of-labor lesson about what LLMs can and can't do in qualitative research