Cross-LLM Behavioral Ethnography

Priority registration for the concept of Cross-LLM Behavioral Ethnography within the AUGMANITAI framework (Ehstand 2025–2026). Author ORCID: 0009-0006-3773-7796. Cross-LLM Behavioral Ethnography designates the systematic, qualitative-empirical study of behavioral differences across LLM systems using controlled inputs, thick description, and native taxonomic categories derived from sustained observation. Addresses the methodological gap between Machine Behaviour (Rahwan et al. 2019, Nature), Machine Psychology (Hagendorff 2023), Behavioral Fingerprinting (Pei et al. 2025), and Synthetic Ethnography (De Seta et al. 2024). Full definitions maintained in the unpublished AUGMANITAI Compendium. ISO 704 / ISO 1087 / ISO 30042 compliant.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-22
DOI
https://doi.org/10.5281/zenodo.18728019
Primary Topic
Authorship Attribution and Profiling
Type
article
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article

Cross-LLM Behavioral Ethnography

Andreas Ehstand
Zenodo (CERN European Organization for Nuclear Research)
Authorship Attribution and Profiling
article

Cross-LLM Behavioral Ethnography

Andreas Ehstand
article en

Abstract

Priority registration for the concept of Cross-LLM Behavioral Ethnography within the AUGMANITAI framework (Ehstand 2025–2026). Author ORCID: 0009-0006-3773-7796. Cross-LLM Behavioral Ethnography designates the systematic, qualitative-empirical study of behavioral differences across LLM systems using controlled inputs, thick description, and native taxonomic categories derived from sustained observation. Addresses the methodological gap between Machine Behaviour (Rahwan et al. 2019, Nature), Machine Psychology (Hagendorff 2023), Behavioral Fingerprinting (Pei et al. 2025), and Synthetic Ethnography (De Seta et al. 2024). Full definitions maintained in the unpublished AUGMANITAI Compendium. ISO 704 / ISO 1087 / ISO 30042 compliant.

Zenodo (CERN European Organization for Nuclear Research)
Reduced inequalities
Openalex Percentile: Top 50%
Authorship Attribution and Profiling
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