解码复杂性：探究质性编码中的人机一致性
Decoding Complexity: Exploring Human-AI Concordance in Qualitative Coding
AI总结：
本研究探究大语言模型在不同复杂度质性编码任务中的应用，发现人机均面临复杂编码挑战，提出需逐任务评估LLMs的集成可行性，并分析了复杂度影响因素。
摘要：
质性数据分析可揭示非结构化数据背后的认知与体验。但编码过程耗时，尤其针对大型数据集，因此亟需引入大语言模型（LLMs）等创新方法。本短文展示了一项初步研究结果：在真实数据集上探究LLMs在不同复杂度编码任务中的集成应用。结果表明，无论对人类编码者还是LLMs，使用大型编码手册与复杂语境的编码都存在固有挑战，将LLMs融入编码流程需按任务逐一评估。我们分析了影响编码任务复杂度的因素，并就质性研究中引入LLMs的效用与局限性展开讨论。
英文摘要：
Qualitative data analysis provides insight into the underlying perceptions and experiences within unstructured data. However, the time-consuming nature of the coding process, especially for larger datasets, calls for innovative approaches, such as the integration of Large Language Models (LLMs). This short paper presents initial findings from a study investigating the integration of LLMs for coding tasks of varying complexity in a real-world dataset. Our results highlight the challenges inherent in coding with extensive codebooks and contexts, both for human coders and LLMs, and suggest that the integration of LLMs into the coding process requires a task-by-task evaluation. We examine factors influencing the complexity of coding tasks and initiate a discussion on the usefulness and limitations of incorporating LLMs in qualitative research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
