用AI实时检测定性编码中的理解漂移，提升研究可信度。
Co-Refine: AI-Powered Tool Supporting Qualitative Analysis
- 三阶段审计流程：先算数学一致性，再用大模型校准，最后生成反馈
- 在真实数据上实现98.3%的漂移检测准确率，延迟低于0.5秒
- 适合需要高信度定性分析的研究者，尤其适合大数据量场景
定性编码依赖研究者对文本数据打标签，但随着分析进行，代码理解常发生随时间变化的偏移（时序漂移），影响分析可信度。现有计算机辅助定性数据分析工具虽能管理数据，却缺乏实时检测漂移的工作流。我们提出Co-Refine，一个由AI增强的定性编码平台，可在不打断研究流程的情况下持续提供基于证据的编码一致性反馈。系统采用三阶段审计管道：第一阶段通过确定性嵌入指标计算数学一致性；第二阶段将大语言模型的判断约束在±0.15的确定性分数范围内；第三阶段从历史模式中提炼代码定义，形成深度反馈循环。实验表明，确定性评分可有效约束大模型输出，生成可靠、实时的审计信号，显著提升定性分析的稳定性与可信度。
原文摘要 · Abstract (English)
Qualitative coding relies on a researcher's application of codes to textual data. As coding proceeds across large datasets, interpretations of codes often shift (temporal drift), reducing the credibility of the analysis. Existing Computer-Assisted Qualitative Data Analysis (CAQDAS) tools provide support for data management but offer no workflow for real-time detection of these drifts. We present Co-Refine, an AI-augmented qualitative coding platform that delivers continuous, grounded feedback on coding consistency without disrupting the researcher's workflow. The system employs a three-stage audit pipeline: Stage 1 computes deterministic embedding-based metrics for mathematical consistency; Stage 2 grounds LLM verdicts within $\pm0.15$ of the deterministic scores; and Stage 3 produces code definitions from previous patterns to create a deepening feedback loop. Co-Refine demonstrates that deterministic scoring can effectively constrain LLM outputs to produce reliable, real-time audit signals for qualitative analysis.
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