用向量聚类与多智能体协作，让质性研究在小时级完成且保持深度。
Neo-Grounded Theory: A Methodological Innovation Integrating High-Dimensional Vector Clustering and Multi-Agent Collaboration for Qualitative Research
- 将1536维向量聚类与多智能体并行编码结合，实现自动化分析。
- 速度提升168倍（3小时对比3周），成本降低96%至500美元。
- 人机协同可发现手动编码无法察觉的身份分裂现象，适合社科研究者。
目的：新扎根理论（NGT）融合向量聚类与多智能体系统，解决质性研究规模与深度的矛盾，可在数小时内完成海量数据的分析并保持解释严谨性。方法：将NGT与人工编码及ChatGPT辅助分析对比，使用4万字中文访谈文本。NGT采用1536维嵌入、层次聚类与并行智能体编码，通过纯自动化与人机协同两个实验验证。结果：NGT实现168倍提速（3小时对3周），质量更优（0.904对0.883），成本下降96%。仅自动化产生抽象框架，而人机协同生成可操作的双路径理论；系统识别出人工编码无法察觉的身份分裂现象。贡献：证明计算客观性与人类解释互补，向量表征提供可复现的语义度量，同时保留意义解释维度。研究者从机械编码转向理论指导，AI负责模式识别，人类提供创造洞察。影响：成本从5万美元降至500美元，使社区能自研自身问题；实时分析使质性洞察与事件同步，表明计算方法可增强而非削弱质性研究的人文关怀。
原文摘要 · Abstract (English)
Purpose: Neo Grounded Theory (NGT) integrates vector clustering with multi agent systems to resolve qualitative research's scale depth paradox, enabling analysis of massive datasets in hours while preserving interpretive rigor. Methods: We compared NGT against manual coding and ChatGPT-assisted analysis using 40,000 character Chinese interview transcripts. NGT employs 1536-dimensional embeddings, hierarchical clustering, and parallel agent-based coding. Two experiments tested pure automation versus human guided refinement. Findings: NGT achieved 168-fold speed improvement (3 hours vs 3 weeks), superior quality (0.904 vs 0.883), and 96% cost reduction. Human AI collaboration proved essential: automation alone produced abstract frameworks while human guidance yielded actionable dual pathway theories. The system discovered patterns invisible to manual coding, including identity bifurcation phenomena. Contributions: NGT demonstrates computational objectivity and human interpretation are complementary. Vector representations provide reproducible semantic measurement while preserving meaning's interpretive dimensions. Researchers shift from mechanical coding to theoretical guidance, with AI handling pattern recognition while humans provide creative insight. Implications: Cost reduction from \$50,000 to \$500 democratizes qualitative research, enabling communities to study themselves. Real-time analysis makes qualitative insights contemporaneous with events. The framework shows computational methods can strengthen rather than compromise qualitative research's humanistic commitments. Keywords: Grounded theory; Vector embeddings; Multi agent systems; Human AI collaboration; Computational qualitative analysis
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