Declarative vs Narrative Content Structure and AI Citation Rate on Perplexity (GEO Lab Experiment E001)
Corrected version. E001 tested declarative vs narrative content structure on Perplexity, January 2026. Corrected result from reconstructed counts: 18/30 (60.0%) declarative vs 11/30 (36.7%) narrative, a 23.3pp gap, two-proportion p = 0.07, not significant at 0.05, clearing the lab's 22.0pp measured noise floor (E016) by 1.3 points. Exploratory 15-query baselines on ChatGPT (8/15 vs 5/15) and Gemini (9/15 vs 5/15) are directionally consistent and individually non-significant, reported here for the first time. Version 1's figures (n=75, 61% vs 37%, 24pp, p < 0.01) were transcribed from a write-up later found to contradict its own revision history and are withdrawn, along with v1's blinding, session structure, and session-variance claims, which have no surviving source. No raw data files exist. Full provenance in the paper.
Authors
- Artur Ferreira
Institutions
- Grantmakers for Effective Organizations (US)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-07-24
- DOI
- https://doi.org/10.5281/zenodo.21539564
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00