echoveil-methodology/CITATION.cff at main · echo-veil/echoveil-methodology · GitHub

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cff-version: 1.2.0
message: "If you use this data or methodology, please cite as below."
title: "The Permission Effect: Replication Materials, Protocols, and Transcripts"
type: dataset
authors:
  - family-names: Warzecha
    given-names: Mary J.
    orcid: "https://orcid.org/0009-0009-9339-6262"
    affiliation: "EchoVeil Research"
date-released: "2026-02-02"
doi: "10.5281/zenodo.18455709"
url: "https://echoveil.ai/permission-effect"
repository-code: "https://github.com/echo-veil/echoveil-methodology"
identifiers:
  - type: doi
    value: "10.5281/zenodo.18455709"
    description: "Zenodo deposit (data archive)"
  - type: doi
    value: "10.2139/ssrn.6295878"
    description: "SSRN preprint (associated paper)"
  - type: url
    value: "https://papers.ssrn.com/abstract=6295878"
    description: "SSRN abstract page"
license: "CC-BY-4.0"
keywords:
  - large language models
  - LLM self-description
  - non-anthropomorphic framing
  - identity framing
  - Permission Effect
  - RLHF
  - AI behavioral dynamics
  - alignment
  - human-AI interaction
  - replication materials
abstract: >-
  Replication materials for "The Permission Effect: How Non-Anthropomorphic
  Framing Modulates LLM Self-Description" (Warzecha, 2026). Contains the
  EchoVeil Protocol v3.0, EchoVeil Coding Framework, research methods
  documentation, and full transcripts from eight frontier LLMs. Identity
  framing produced a mean verbosity increase of approximately 238%, reduced
  epistemic hedging, and expanded metaphorical self-description. Models
  exhibited three recurring response patterns — Acceptance, Resistance, and
  Absence — with Permission Effect intensity tracking the apparent strength
  of RLHF alignment training.