[论文] Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemical...

论文概要

研究领域: ML
作者: Clarisse de Souza, Gabriel Barbosa, Simone Diniz Junqueira Barbosa
发布时间: 2025-06-01
arXiv: 2506.00635

中文摘要

大语言模型正在重塑研究实践，同时悄然侵蚀研究者的认知问责性。本文评论引入PEEL——AI认知参与素养协议，一种工作脚手架，将基于Voyant Tools的确定性远读与通过Claude的LLM解释相结合，扎根于皮尔士符号学和溯因推理。将其应用于三篇源文本的AI生成浓缩版本，PEEL揭示了在数量、词频和认知声音方面的系统性扭曲——这些扭曲在没有非AI测量的情况下是不可见的——并得出三项设计启示：确定性工具必须伴随AI工具；流畅不等于忠实；认知权威必须被设计进去，而非被假定。

原文摘要

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.

自动采集于 2026-06-05

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