让大模型分析过程可审计，提升质性研究透明度。
Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research
- 逐段独立处理数据，保留每步提示与输出
- 案例显示能发现模型与人判断的系统差异
- 适合追求可复现性的质性研究者使用
大语言模型在质性数据分析中应用日益广泛，但许多工作流程隐藏了分析结论的生成过程。我们提出 QualAnalyzer，一个开源的 Chrome 扩展工具，集成于 Google Workspace，支持原子级的 LLM 分析：对每个数据单元独立处理，并完整保留其提示、输入和输出。通过两个案例研究——整体论文评分与访谈文本的演绎式主题编码——我们验证了该方法能生成清晰的审计轨迹，帮助研究者识别模型与人类判断之间的系统性差异。我们认为，过程可审计性是提升 LLM 辅助质性研究透明度与方法严谨性的关键。
原文摘要 · Abstract (English)
Large language models are increasingly used for qualitative data analysis, but many workflows obscure how analytic conclusions are produced. We present QualAnalyzer, an open-source Chrome extension for Google Workspace that supports atomistic LLM analysis by processing each data segment independently and preserving the prompt, input, and output for every unit. Through two case studies -- holistic essay scoring and deductive thematic coding of interview transcripts -- we show that this approach creates a legible audit trail and helps researchers investigate systematic differences between LLM and human judgments. We argue that process auditability is essential for making LLM-assisted qualitative research more transparent and methodologically robust.
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