用图灵思想重审大模型做质性分析的争议，关键不是能否，而是是否可比人类。
Can machines perform a qualitative data analysis? Reading the debate with Alan Turing
- 以图灵思想重构争论焦点，从‘能否’转向‘是否可比’。
- 通过实证与批判反思，指出当前反对意见聚焦错误问题。
- 用图灵式写作风格回应反对观点，强化论证逻辑。
本文反思了学界拒绝在质性数据分析中使用大语言模型（LLMs）的文献。通过实证证据和批判性思考，说明当前的批评争论聚焦于错误的问题。论文提出，研究大语言模型用于质性分析的重点不在于方法本身，而在于对人工系统执行分析的实证探究。文章基于艾伦·图灵的奠基性工作《计算机器与智能》，以图灵的核心思想重新解读当前争论。因此，本文将质性分析中的大模型讨论从‘机器能否’转变为‘大模型能否产生与人类分析师相当的分析结果’。最后部分以图灵式的写作和修辞风格，分析了反对使用大模型进行质性分析的观点，从而深化对反面立场的理解。
原文摘要 · Abstract (English)
This paper reflects on the literature that rejects the use of Large Language Models (LLMs) in qualitative data analysis. It illustrates through empirical evidence as well as critical reflections why the current critical debate is focusing on the wrong problems. The paper proposes that the focus of researching the use of the LLMs for qualitative analysis is not the method per se, but rather the empirical investigation of an artificial system performing an analysis. The paper builds on the seminal work of Alan Turing and reads the current debate using key ideas from Turing "Computing Machinery and Intelligence". This paper therefore reframes the debate on qualitative analysis with LLMs and states that rather than asking whether machines can perform qualitative analysis in principle, we should ask whether with LLMs we can produce analyses that are sufficiently comparable to human analysts. In the final part the contrary views to performing qualitative analysis with LLMs are analysed using the same writing and rhetorical style that Turing used in his seminal work, to discuss the contrary views to the main question.
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