用GPT-4 Turbo做主题分析，准确度接近人类，还能更抽象地归纳主题。
Optimizing Generative AI's Accuracy and Transparency in Inductive Thematic Analysis: A Human-AI Comparison
- 用分步提示词脚本让AI逐步编码，过程可追踪可复现。
- 编码准确率与普通人类分析师相当，能有效识别主题。
- 在解释层面超越人类，能给出更宏观抽象的理论视角。
本研究探讨了生成式AI在归纳式主题分析中的透明性与准确性，采用GPT-4 Turbo API结合分步提示词的Python脚本实现。该方法确保编码过程可追踪、系统化，生成带支持语句和页码引用的编码结果，提升了验证与复现性。结果显示，GenAI的归纳编码表现与平均人类编码者相当，能有效识别主题；在解释层面，其能将主题置于更广泛的概念框架中，提供更具概括性和抽象性的解读，优于人类编码者的局部理解。
原文摘要 · Abstract (English)
This study highlights the transparency and accuracy of GenAI's inductive thematic analysis, particularly using GPT-4 Turbo API integrated within a stepwise prompt-based Python script. This approach ensured a traceable and systematic coding process, generating codes with supporting statements and page references, which enhanced validation and reproducibility. The results indicate that GenAI performs inductive coding in a manner closely resembling human coders, effectively categorizing themes at a level like the average human coder. However, in interpretation, GenAI extends beyond human coders by situating themes within a broader conceptual context, providing a more generalized and abstract perspective.
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