用AI筛选高价值文本，让质性分析效率翻倍。
Big Meaning: Qualitative Analysis on Large Bodies of Data Using AI
- 用AI生成描述性代码，筛选潜在意义丰富的文本
- 人工编码显示AI选文的代码密度达随机选文两倍
- 适合需要高效挖掘深层含义的研究者
本研究提出一种框架，利用AI生成的描述性代码来衡量文本的‘丰度’——即独特人类编码的密度，用于主题分析。AI不替代人工解读，而是引导选择更可能产生丰富质性洞见的文本。基于2,530篇马来西亚难民态度新闻文章的数据集，我们让三位独立编码员对AI选文与随机选文进行编码比较。结果表明，AI选文的代码丰度约为随机选文的两倍。研究支持将AI生成代码作为识别高意义生成潜力文本的有效代理指标。
原文摘要 · Abstract (English)
This study introduces a framework that leverages AI-generated descriptive codes to indicate a text's fecundity--the density of unique human-generated codes--in thematic analysis. Rather than replacing human interpretation, AI-generated codes guide the selection of texts likely to yield richer qualitative insights. Using a dataset of 2,530 Malaysian news articles on refugee attitudes, we compare AI-selected documents to randomly chosen ones by having three human coders independently derive codes. The results demonstrate that AI-selected texts exhibit approximately twice the fecundity. Our findings support the use of AI-generated codes as an effective proxy for identifying documents with a high potential for meaning-making in thematic analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。