用大模型自动构建质性研究理论，省去人工编码瓶颈
LOGOS: LLM-driven End-to-End Grounded Theory Development and Schema Induction for Qualitative Research
- 用大模型+图推理实现从文本到理论的全流程自动化
- 在5个数据集上平均达80.4%与专家理论对齐度
- 适合希望高效开展质性研究的学者或跨领域团队
扎根理论能深入挖掘质性数据洞察，但依赖专家手动编码，难以规模化。现有计算工具或无法完全自动化，或缺乏灵活的编码体系构建能力。我们提出LOGOS，一个端到端框架，可将原始文本全自动转化为结构化、分层化的理论体系。LOGOS融合大模型编码、语义聚类、图推理及一种新颖的迭代优化流程，生成高度可复用的编码手册。为确保公平比较，我们还引入五维评估指标和标准化训练-测试划分协议。在五个不同语料库上，LOGOS持续优于强基线，在复杂数据集上平均达到80.4%与专家构建理论的对齐度。LOGOS展现了在不牺牲理论深度的前提下，推动质性研究民主化与规模化的潜力。
原文摘要 · Abstract (English)
Grounded theory offers deep insights from qualitative data, but its reliance on expert-intensive manual coding presents a major scalability bottleneck. Existing computational tools either fail on full automation or lack flexible schema construction. We introduce LOGOS, a novel, end-to-end framework that fully automates the grounded theory workflow, transforming raw text into a structured, hierarchical theory. LOGOS integrates LLM-driven coding, semantic clustering, graph reasoning, and a novel iterative refinement process to build highly reusable codebooks. To ensure fair comparison, we also introduce a principled 5-dimensional metric and a train-test split protocol for standardized, unbiased evaluation. Across five diverse corpora, LOGOS consistently outperforms strong baselines and achieves a remarkable average $80.4\%$ alignment with an expert-developed schema on complex datasets. LOGOS demonstrates a potential to democratize and scale qualitative research without sacrificing theoretical nuance.
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