Entity Density and Content Structure as Independent Levers on AI Citation Under Forced Retrieval: A Preregistration
A preregistered protocol (no results) testing whether entity density and content structure independently affect AI-engine citation under forced retrieval, across ChatGPT, Claude, Perplexity and Gemini answer-time agents. Registered before confirmatory data collection. Includes a 2x2 design, a synthetic control entity, binary citation scoring, a conservative noise threshold, an independent verification subset, and a declared competing interest.
Authors
- Artur Ferreira
- Julio Arévalo Piedra
Institutions
- Knowledge Unlatched (Germany) (DE)
- Grantmakers for Effective Organizations (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-07-30
- DOI
- https://doi.org/10.5281/zenodo.21705818
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- preprint