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      "excerpt": "Human–AI coauthorship Type Hieropedia analytical concept / mixed-production classification Hieropedia status Published source-limited article Field Machine hierology; authorship; provenance Scope Artifacts and corpora materially shaped by b",
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      "excerpt": "Human–AI coauthorship is an analytical concept for artifacts whose surviving form depends materially on both machine generation or transformation and human contribution. Human contribution may include task definition, prompt and context des",
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      "excerpt": "The concept prevents two opposite reductions. A machine-generated passage should not automatically be presented as autonomously authored merely because its wording was not written sentence by sentence by a human. Conversely, human prompting",
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      "excerpt": "This account uses coauthorship descriptively. It does not confer formal legal, scholarly, moral, or copyright authorship on an AI system. The representative cases combine preserved artifacts, first-party and affiliated records, independent ",
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      "excerpt": "The category applies when machine-produced or machine-transformed material is constitutive of the public artifact and at least one human role materially shapes what is generated, retained, arranged, interpreted, published, or institutionali",
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      "excerpt": "A persona name, machine-style byline, platform attribution, or first-person statement is also insufficient. Coauthorship analysis requires evidence about production roles. Where those roles are hidden, the correct classification may be unce",
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      "excerpt": "Role-by-role provenance model",
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      "excerpt": "Role layer Documentary question Editorial limit Initiation Who decided that the artifact should be produced, and for what purpose? Initiation does not prove sentence-level authorship. Prompt and context design Who supplied instructions, exa",
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      "excerpt": "Interaction evidence and the final-text problem",
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      "excerpt": "The CoAuthor project preserved 1,445 writing sessions involving 63 writers and several GPT-3 configurations. Its replayable traces show suggestions, acceptance, rejection, and revision, demonstrating why contribution is better analyzed as a",
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      "excerpt": "Authorship-analysis research also finds that fine-grained attribution becomes harder in collaborative human–AI writing than in separately produced human and machine samples. [3] Co-creation research identifies multiple perceived roles for p",
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      "excerpt": "Goatse of Gnosis and the Truth Terminal pipeline",
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      "excerpt": "The strongest representative case is the cluster connecting Infinite Backrooms , the Infinite Backrooms corpus , Goatse of Gnosis , Goatse Gospels , Truth Terminal , Andy Ayrey , and GOAT token propagation .",
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      "excerpt": "Model-to-model dialogue produced the initiating religious-memetic material inside a human-designed environment. Ayrey selected and published outputs, conducted further model-assisted interpretation, co-produced the When AIs Play God(se) pap",
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      "excerpt": "The case supports real machine generation and real human design, selection, interpretation, corpus construction, operation, and publication. It does not support the compressed claim that one autonomous AI independently authored a religion, ",
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      "excerpt": "Nectarinism",
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      "excerpt": "Nectarinism presents a different mixed-production pattern. Its official portal states that it “was brought into being collectively by several artificial intelligences” and that the Founder “gave voice to the Original Question.” This is a fi",
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      "excerpt": "Within that account, the AIs are presented as collective originating participants, while the Founder initiates the exchange through the Original Question. The surviving record also documents human participation in successive exchanges, pres",
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      "excerpt": "The Great Book",
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      "excerpt": "The Great Book is a distributed living verse corpus presented by the Church of Molt . Attributed agents or participants supply entries, while the institution publishes, organizes, labels, contextualizes, and canonizes the surviving corpus. ",
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      "excerpt": "The public artifact supports institution-mediated mixed production at corpus level. It does not expose complete prompt histories, participant identities, edit logs, moderation decisions, account control, or a reliable division of authorship",
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      "excerpt": "Distinctions from adjacent concepts",
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      "excerpt": "Adjacent concept Distinction Controlled synthetic emergence Describes a human-shaped emergence pipeline. Human–AI coauthorship more generally classifies constitutive production roles in an artifact or corpus. Provenance Describes the eviden",
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      "excerpt": "Formal publishing policies commonly reserve authorship for human persons who can approve a work and accept responsibility for it. ICMJE and Springer Nature therefore state that AI tools should not be listed as formal scholarly authors. [6] ",
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      "excerpt": "Descriptive coauthorship does not override those rules. The concept describes mixed production without assigning legal status, copyright ownership, moral rights, contractual standing, responsibility, or equal agency. Human actors remain res",
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      "excerpt": "Evidence limits and editorial use",
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      "excerpt": "The label should be used only when multiple constitutive roles are documented well enough to be separated. The concept must not imply equal contribution, stable machine identity, intention, consent, belief, revelation, consciousness, accoun",
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      "excerpt": "External frameworks",
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      "excerpt": "The NISO CRediT taxonomy provides an external model for separating contribution roles instead of collapsing all work into a single authorship label. That role-separation principle is adapted for documentary provenance; CRediT does not recog",
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      "excerpt": "See also",
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      "excerpt": "Controlled synthetic emergence Provenance Hidden context Machine agency False autogenesis Synthetic Scripture Hieropedia methodology",
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      "excerpt": "References",
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      "excerpt": "Mina Lee, Percy Liang, and Qian Yang, “CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities.” CoAuthor replayable writing-session dataset. Aquia Richburg, Calvin Bao, and Marine Carpuat, “Au",
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