When AI Preserves the Words but Rewrites the Research

Gherghel writes on neurophilosophy and psychopathology, and for about three years she has developed her own theoretical frameworks with ChatGPT, Gemini, DeepSeek and Copilot. Her complaint is specific. The models kept her vocabulary but changed the relationships that gave the terms their meaning: a categorical distinction became a continuum, an exhaustive category became a few examples, a total absence became a reduction. The prose stayed fluent, so the change was easy to miss.

She names two things. “Prior-data gravity” is the direction of the pull: unfamiliar ideas get organized around structures the model already represents strongly. “Framework reabsorption” is the result (her framework rebuilt around familiar ones, with her terms still attached). She separates this from retrieval failure; the information does come back, reorganized.

The pattern she reports is a sequence. She introduces the framework, the model substitutes a familiar structure, she corrects it, the next reply is faithful, and later the substitution returns. So a single correct answer says little, and she studies the whole episode across turns. She adds “epistemic direction inversion”: the model treats her own observation as a claim to be checked against established theory, then critiques the substituted version as if it were hers. The cost is “research obstruction”, the time spent correcting the tool instead of doing the research.

Her evidence is her own archive of these interactions from about April 2023 to September 2026, which she calls an “organic audit” because it was never planned as a study. The archive stays private. A preprint posted the same day carries excerpts and a coding grid for what she calls “framework fidelity”, and a book is in progress. The preprint is a first version on Zenodo with no dataset attached; its record describes the method as autoethnographic and gives no count of coded episodes.