Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
LLMs are reshaping research while eroding epistemic accountability. The PEEL framework combines deterministic distant reading with LLM interpretation, grounded in Peircean semiotics, to reveal systematic distortions in AI-generated text. Key implications: deterministic tools must accompany AI, fluency does not equal fidelity, and epistemic authority must be designed in.
[2606.04152] Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
[Submitted on 2 Jun 2026]
Title:Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research
View a PDF of the paper titled Thinking Through Signs: PEEL as a Semiotic Scaffolding for Epistemically Accountable AI-Enabled Research, by Clarisse de Souza and 4 other authors
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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.
Comments: 10 pages, 5 figuras
Subjects:
Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
ACM classes: H.3.3; I.2.0
Cite as: arXiv:2606.04152 [cs.AI]
(or arXiv:2606.04152v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.04152
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Juliana Ferreira J [view email] [v1] Tue, 2 Jun 2026 19:19:52 UTC (1,821 KB)
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