Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
Quick Take
The PEEL framework combines deterministic reading with LLM interpretation to enhance epistemic accountability in AI research, revealing hidden distortions in AI-generated text. It emphasizes the need for deterministic tools alongside AI, challenges assumptions about fluency vs. fidelity, and insists on intentional design of epistemic authority.
Key Points
- PEEL integrates Voyant Tools for distant reading and Claude for LLM interpretation.
- It identifies systematic distortions in AI-generated text that are often overlooked.
- Three design implications stress the need for deterministic tools with AI.
- The framework questions the assumption that fluency equates to fidelity.
- Epistemic authority must be intentionally designed into AI research processes.
Article Excerpt
From source RSS / original summaryarXiv:2606. 04152v1 Announce Type: new Abstract: 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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