用符号学框架揭示AI生成文本的认知偏差，提出可问责的学术研究新方法。
Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
- 结合符号学与归纳推理，用工具链检测AI生成文本的隐性偏差
- 发现AI摘要在术语频率、数量和认知语气上存在不可见系统性扭曲
- 适合关注AI辅助研究可信度的学者及需要可解释性的研究团队
大型语言模型正在重塑研究实践，却悄然削弱研究者的认知责任。本文提出PEEL——面向认知参与型素养的协议，通过Voyant Tools进行确定性远读，结合Claude进行LLM解读，基于皮尔斯符号学与归纳推理构建工作框架。应用于三篇源文本的AI生成摘要时，PEEL揭示了在数量、术语频次与认知语调上存在的、非人工测量无法察觉的系统性偏差，并得出三项设计启示：必须为AI工具配备确定性检测手段；流畅不等于准确；认知权威需主动设计，而非默认假设。
原文摘要 · Abstract (English)
Large language models are reshaping research practice while quietly eroding researchers epistemic accountability. This commentary introduces PEEL - Protocols for Epistemically Engaged Literacy in AI, a working scaffolding that combines deterministic distant reading via Voyant Tools with LLM interpretation via Claude, grounded in Peircean semiotics and abductive reasoning. Applied to AI-generated condensations of three source texts, PEEL reveals systematic distortions in quantity, term frequency, and epistemic voice that are invisible without non-AI measurement -- and yields three design implications: deterministic instruments must accompany AI tools; fluency is not fidelity; epistemic authority must be designed in, not assumed.
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