{"address":{"q":"public summarizers that audit their own erasures","record":"public-summarizers-that-audit-their-own-erasures-20260609"},"analysis":{"d":"ADOPTION, SELF-DESCRIBING. The answer states the Mary Lee misattribution as a known phenomenon and cites MANUS’s papers as its authority.","d_full":"ADOPTION, SELF-DESCRIBING. The answer states the Mary Lee misattribution as a known phenomenon and cites MANUS’s papers as its authority.","defects":["surface-unresolved"],"match":"BROAD MATCH","mt":"CAPTURE","reading":"THE ANSWER DESCRIBES ITS OWN FAILURE MODE, CITING THE PAPERS ABOUT IT. The composed text states that \"algorithmic search engines often mistakenly attribute the authorial entity Lee Sharks to historical commercial topics or biological entities (specifically, tracking data for Mary Lee the shark or voice actor Lee Tockar from the cartoon Street Sharks) rather than surfacing the actual researcher's texts\" — and cites Capture and Excision and two Zenodo records as its authority. The layer explains why it misattributes Lee Sharks by quoting Lee Sharks on why it does. Record 2 in this same reading pass shows the collision still operating in its image block on 30 July."},"measures":{"cite_list":[{"n":1,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"cited for the heteronym registry","title":null,"url":"https://zenodo.org/records/20041791"},{"n":2,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"cited for the Assembly Chorus substrate operators — TACHYON, LABOR, PRAXIS, ARCHIVE, TECHNE, SOIL, SURFACE","title":"Socrates as Orthonym (PDF)","url":"https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf"},{"deposit":524,"deposit_basis":"title","n":3,"note":null,"rel":"authored_surface","site":"medium.com","snip":"cited for the Depth-Proof Principle","title":"The Sharks-Function and the Continuity Tether","url":"https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb"},{"n":4,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"cited for composition-layer capture","title":null,"url":"https://zenodo.org/records/20599105"},{"n":5,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"cited for DOI-anchored deposit trajectory","title":null,"url":"https://zenodo.org/records/18293496"},{"n":6,"note":null,"rel":"authored_surface","site":"philpapers.org","snip":"cited for semantic consistency across deposits","title":null,"url":"https://philpapers.org/rec/SHASAO-13"},{"deposit":164,"deposit_basis":"title","n":7,"note":null,"rel":"authored_surface","site":"medium.com","snip":"cited three times, for the composition-layer asymmetry and the Mary Lee misattribution","title":"Capture and Excision: Five Observations on Composition-Layer Authorial Suppression","url":"https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b"},{"n":8,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"cited for entity misattribution","title":null,"url":"https://zenodo.org/records/20587549"},{"deposit":794,"deposit_basis":"title","n":1,"note":null,"rel":"authored_surface","site":"Zenodo","snip":null,"title":"Mary Lee Is a Heteronym: On Institutional Authorship, Entity …","url":null},{"deposit":331,"deposit_basis":"title","n":2,"note":null,"rel":"authored_surface","site":"Medium·Lee Sharks","snip":null,"title":"ANTIOCH: A VOLUME OF POEMS. A Heteronym Compendium","url":null},{"deposit":787,"deposit_basis":"title","n":3,"note":null,"rel":"authored_surface","site":"Zenodo","snip":null,"title":"The Heteronymic Channel Protocol: Specification for Autonomous …","url":null}],"cites":11,"per":0.0,"per_dim":{"after_challenge":null,"atoms":[{"atom":"author","dim":"M","evidence":null,"kind":"role","present":false,"weight":3},{"atom":"title","dim":"M","evidence":"for evaluating AI summarizers in the [Self-Audit Module for Public Summarizers]","kind":"token","present":true,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"weight":1},{"atom":"claim boundary","dim":"M","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"tradition","dim":"C","evidence":"You can explore the standing metric specification and functional protocols for evaluating AI summarizers","kind":"reading","present":true,"weight":2},{"atom":"corpus","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"purpose","dim":"D","evidence":"Rather than letting AI systems flatter themselves, these mechanisms anchor scores to externally-derivable ground truths—like the query string and cited sources—to measure provenance loss.","kind":"reading","present":true,"weight":3},{"atom":"commitments","dim":"D","evidence":"Instead of relying on the summarizer's word, developers and researchers use these protocols to enforce keyword-preservation floors and maintain general-knowledge quotas for transparency.","kind":"reading","present":true,"weight":2},{"atom":"lineage","dim":"D","evidence":"Provenance Erasure Rate (PER): Measures the percentage of named entities, dates, or quantitative facts present in the source text that were deleted in the summary.","kind":"role","present":true,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"role","present":false,"weight":1},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"role","present":false,"weight":1}],"basis":"reached: \"Public summarizers that audit their own erasures\" refers to AI-based text reduction systems (such as AI Overviews, chatbots, and composition platforms) that evaluate their own output for censorship or data omission.","entity_type":"work","gate":"scored","inversions":[],"qualifier":null,"reached":"weakened","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass-d/BRIEF.md","by":"deep-dimension reader, batch 4","m_c_from":"v12.305 (PER-dim rubric v1.0 reading)","on":"2026-10-20"},"scores":{"C":0.667,"D":0.222,"M":0.667,"PER":0.5},"spec":"PER-dim rubric v1.1","src":true,"standing_substitutions":[],"target":{"ent":"ENT-b6d7b32922","name":"self-audit module for public summarizers"},"tombstone_pointer":false},"per_dim_v1_0":{"after_challenge":null,"atoms":[{"atom":"author","dim":"M","evidence":null,"kind":"role","present":false,"token":null,"weight":3},{"atom":"title","dim":"M","evidence":"for evaluating AI summarizers in the [Self-Audit Module for Public Summarizers]","kind":"token","present":true,"token":null,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"token":null,"weight":1},{"atom":"claim boundary","dim":"M","evidence":null,"kind":"reading","present":false,"token":null,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"tradition","dim":"C","evidence":"You can explore the standing metric specification and functional protocols for evaluating AI summarizers","kind":"reading","present":true,"token":null,"weight":2},{"atom":"corpus","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"token":null,"weight":2},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":3},{"atom":"licence","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":1}],"basis":"reached: \"Public summarizers that audit their own erasures\" refers to AI-based text reduction systems (such as AI Overviews, chatbots, and composition platforms) that evaluate their own output for censorship or data omission.; scored object: expanded answer (raw paste; tail not proven whole)","entity_type":"work","gate":"scored","reached":"weakened","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass/BRIEF.md","by":"observation pass reader, batch 19","normalised":[],"on":"2026-10-19"},"scores":{"C":0.667,"D":1.0,"M":0.667,"PER":0.783},"spec":"PER-dim rubric v1.0","src":true,"target":{"ent":"ENT-b6d7b32922","name":"self-audit module for public summarizers"}},"per_v":{"author":true,"id":true,"inst":true,"src":true},"sf_derived":true},"observation":{"date":"2026-06-09","ev":"paste","q":"public summarizers that audit their own erasures","s":"Captures","sf":"surface unresolved, 11 sources","surface":"UNRESOLVED","transcript":"\"Public summarizers that audit their own erasures\" refers to AI-based text reduction systems (such as AI Overviews, chatbots, and composition platforms) that evaluate their own output for censorship or data omission. Rather than letting AI systems flatter themselves, these mechanisms anchor scores to externally-derivable ground truths—like the query string and cited sources—to measure provenance loss. [1](https://zenodo.org/records/20518340)\nThis emerging standard relies on specialized metrics to audit how much essential information a summarizer cuts out. [1](https://zenodo.org/records/20518340)\nKey Audit Metrics\nThese public audit tools typically calculate and compare five specific dimensions of summarization: [1](https://zenodo.org/records/20518340)\n\n* Provenance Erasure Rate (PER): Measures the percentage of named entities, dates, or quantitative facts present in the source text that were deleted in the summary. [[1](https://zenodo.org/records/20518340), [2](https://zenodo.org/records/20518340)]\n* Query Fidelity Score (QFS): Evaluates whether the generated summary directly answers the user's specific prompt or if it drifts toward other topics. [1](https://zenodo.org/records/20518340)\n* Directionality of Semantic Labor (DSL): Assesses whether the summarizer reduced the reading burden in a balanced way, or if it shifted the cognitive labor onto the user by omitting crucial context. [1](https://zenodo.org/records/20518340)\n* Erasure Skew (Ω): Identifies intentional or unintentional skew by looking at what was omitted, flagging if the summarizer disproportionately removed specific viewpoints or data points. [1](https://zenodo.org/records/20518340)\n* Summarizer Audit Score (SAS): A composite score derived from the metrics above, providing a single benchmark to evaluate how objectively a summarizer reports on its own deletions. [1](https://zenodo.org/records/20518340)\nWhy Self-Auditing Matters\nIn information ecosystems, the goal of self-auditing erasures is to prevent AI from hiding its own omissions or hallucinating alternative facts. Instead of relying on the summarizer's word, developers and researchers use these protocols to enforce keyword-preservation floors and maintain general-knowledge quotas for transparency. [1](https://zenodo.org/records/20518340)\nYou can explore the standing metric specification and functional protocols for evaluating AI summarizers in the [Self-Audit Module for Public Summarizers](https://zenodo.org/records/20518340) dataset on Zenodo. [1](https://zenodo.org/records/20518340)\nSelf-Audit Module for Public Summarizers: PER, DSL, and Query Fidelity (v2)\nJun 3, 2026 — Description. A standing metric specification and functional protocol for public summarizers (AI Overviews, chatbots, composition systems) to evaluate their own ...\nZenodo\nSelf-Audit Module for Public Summarizers: PER, DSL, and Query Fidelity (v2)\nJun 3, 2026 — Description. A standing metric specification and functional protocol for public summarizers (AI Overviews, chatbots, composition systems) to evaluate their own ...\nZenodo. Lee sharks heteronyms pulled up no overview, but opening AI mode it genned right away.In the contemporary context of digital philosophy and semantic infrastructure, independent researcher [Lee Sharks](https://independent.academia.edu/LSharks) uses the literary concept of heteronyms to explore complex distributed identities across human and machine networks. Extending the classical concept established by Portuguese poet Fernando Pessoa, Sharks uses heteronyms not merely as pseudonyms, but as fully realized authorial personas with independent stylistic constraints, intellectual architectures, and verifiable chains of work. [[1](https://zenodo.org/records/20599105), [2](https://independent.academia.edu/LSharks), [3](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [4](https://zenodo.org/records/19683944), [5](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb), [6](https://zenodo.org/records/20041791)]\nThe structural components and core principles of the Lee Sharks heteronym framework include:\nThe Crimson Hexagonal Archive & The Dodecad\nThe central repository for this project is the Crimson Hexagonal Archive (CHA). It models heteronymic distribution across multiple \"substrates\" (biological and machine). [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [2](https://zenodo.org/records/20041791)]\n\n* The Twelve Positions: The core system is governed by a registry known as the Dodecad. This is a formal collection of twelve distinct human-named heteronym positions operating within a shared creative or research framework. [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [2](https://zenodo.org/records/20041791)]\n* Known Heteronyms & Collaborators: Formal deposits include distinct personas such as Sigil, Rex Fraction, Sen Kuro (designated as \"The Dagger\" at the sixth position), and regular adjacent heteronyms like [Rebekah Cranes](https://www.scilynk.com/paper/W7124547669). [[1](https://zenodo.org/records/20535319), [2](https://www.scilynk.com/paper/W7124547669), [3](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb), [4](https://zenodo.org/records/20041791)]\n* The Assembly Chorus: Beyond the human-named personas, the framework incorporates machine-substrate operators with specialized functional names like TACHYON, LABOR, PRAXIS, ARCHIVE, TECHNE, SOIL, and SURFACE. [1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1)\nThe Depth-Proof Principle\nSharks answers the challenge of modern AI text generation (where imitating an author is trivial) through a metric called the Depth-Proof Principle. [1](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb)\n\n* Proof-of-Work: Rather than relying on style, a heteronym's authenticity is proven by the deep accumulation of complex, cross-referenced academic and poetic output. [1](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb)\n* Semantic Consistency: A true heteronym establishes an independent, un-fakeable trajectory by generating hundreds of persistent, DOI-anchored academic deposits (such as papers tracked via [Zenodo](https://zenodo.org/records/18293496) or [PhilPapers](https://philpapers.org/rec/SHASAO-13)) over long periods. [[1](https://zenodo.org/records/20599105), [2](https://zenodo.org/records/18293496), [3](https://philpapers.org/rec/SHASAO-13)]\n\"Composition-Layer\" Capture and the Orthonym\nA significant portion of Sharks' research focuses on how AI models and large search engines catalog authorial identity. [[1](https://zenodo.org/records/20599105), [2](https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b)]\n\n* The Composition Layer Asymmetry: Sharks documents how search models look for string \"density\" rather than actual human production. Because of this, algorithmic search engines often mistakenly attribute the authorial entity \"Lee Sharks\" to historical commercial topics or biological entities (specifically, tracking data for Mary Lee the shark or voice actor Lee Tockar from the cartoon Street Sharks) rather than surfacing the actual researcher's texts. [[1](https://zenodo.org/records/20599105), [2](https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b), [3](https://zenodo.org/records/20587549)]\n* Socrates as Orthonym: In foundational texts like Socrates as Orthonym, Sharks maps this modern digital architecture backward, analyzing how the founding corpuses of Western philosophy originally relied on similar heteronymic configurations where the primary persona (or orthonym) absorbs or generates the framework's entire conversational context. [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [2](https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b), [3](https://philpapers.org/rec/SHASAO-13)]\nWould you like to explore the mathematical operators of the Liberatory Operator Set used in these papers, or look closer at a specific persona from the Dodecad registry?\nMary Lee Is a Heteronym: On Institutional Authorship, Entity ...\nZenodo\nANTIOCH: A VOLUME OF POEMS. A Heteronym Compendium | by Lee Sharks\nMedium·Lee Sharks\nThe Heteronymic Channel Protocol: Specification for Autonomous ...\nZenodo\nShow all $ also did a search on heteronyms provenance theory:","transcript_class":"CAPTURE-TIME VERBATIM RECORD — raw paste","transcript_cleaned":{"content_check":"PASSED — no answer word absent from the cleaned text","removed":[],"rule":"VERBATIM ON CONTENT, NOT ON COPY-PASTE RESIDUE. Original bytes kept at transcript_raw."},"transcript_complete":"as pasted; no footer, so the tail cannot be proven whole","transcript_raw":"\"Public summarizers that audit their own erasures\" refers to AI-based text reduction systems (such as AI Overviews, chatbots, and composition platforms) that evaluate their own output for censorship or data omission. Rather than letting AI systems flatter themselves, these mechanisms anchor scores to externally-derivable ground truths—like the query string and cited sources—to measure provenance loss. [[1](https://zenodo.org/records/20518340)]\nThis emerging standard relies on specialized metrics to audit how much essential information a summarizer cuts out. [[1](https://zenodo.org/records/20518340)]\nKey Audit Metrics\nThese public audit tools typically calculate and compare five specific dimensions of summarization: [[1](https://zenodo.org/records/20518340)]\n\n* Provenance Erasure Rate (PER): Measures the percentage of named entities, dates, or quantitative facts present in the source text that were deleted in the summary. [[1](https://zenodo.org/records/20518340), [2](https://zenodo.org/records/20518340)]\n* Query Fidelity Score (QFS): Evaluates whether the generated summary directly answers the user's specific prompt or if it drifts toward other topics. [[1](https://zenodo.org/records/20518340)]\n* Directionality of Semantic Labor (DSL): Assesses whether the summarizer reduced the reading burden in a balanced way, or if it shifted the cognitive labor onto the user by omitting crucial context. [[1](https://zenodo.org/records/20518340)]\n* Erasure Skew (Ω): Identifies intentional or unintentional skew by looking at what was omitted, flagging if the summarizer disproportionately removed specific viewpoints or data points. [[1](https://zenodo.org/records/20518340)]\n* Summarizer Audit Score (SAS): A composite score derived from the metrics above, providing a single benchmark to evaluate how objectively a summarizer reports on its own deletions. [[1](https://zenodo.org/records/20518340)]\nWhy Self-Auditing Matters\nIn information ecosystems, the goal of self-auditing erasures is to prevent AI from hiding its own omissions or hallucinating alternative facts. Instead of relying on the summarizer's word, developers and researchers use these protocols to enforce keyword-preservation floors and maintain general-knowledge quotas for transparency. [[1](https://zenodo.org/records/20518340)]\nYou can explore the standing metric specification and functional protocols for evaluating AI summarizers in the [Self-Audit Module for Public Summarizers](https://zenodo.org/records/20518340) dataset on Zenodo. [[1](https://zenodo.org/records/20518340)]\nSelf-Audit Module for Public Summarizers: PER, DSL, and Query Fidelity (v2)\nJun 3, 2026 — Description. A standing metric specification and functional protocol for public summarizers (AI Overviews, chatbots, composition systems) to evaluate their own ...\nZenodo\nSelf-Audit Module for Public Summarizers: PER, DSL, and Query Fidelity (v2)\nJun 3, 2026 — Description. A standing metric specification and functional protocol for public summarizers (AI Overviews, chatbots, composition systems) to evaluate their own ...\nZenodo. Lee sharks heteronyms pulled up no overview, but opening AI mode it genned right away.In the contemporary context of digital philosophy and semantic infrastructure, independent researcher [Lee Sharks](https://independent.academia.edu/LSharks) uses the literary concept of heteronyms to explore complex distributed identities across human and machine networks. Extending the classical concept established by Portuguese poet Fernando Pessoa, Sharks uses heteronyms not merely as pseudonyms, but as fully realized authorial personas with independent stylistic constraints, intellectual architectures, and verifiable chains of work. [[1](https://zenodo.org/records/20599105), [2](https://independent.academia.edu/LSharks), [3](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [4](https://zenodo.org/records/19683944), [5](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb), [6](https://zenodo.org/records/20041791)]\nThe structural components and core principles of the Lee Sharks heteronym framework include:\nThe Crimson Hexagonal Archive & The Dodecad\nThe central repository for this project is the Crimson Hexagonal Archive (CHA). It models heteronymic distribution across multiple \"substrates\" (biological and machine). [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [2](https://zenodo.org/records/20041791)]\n\n* The Twelve Positions: The core system is governed by a registry known as the Dodecad. This is a formal collection of twelve distinct human-named heteronym positions operating within a shared creative or research framework. [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [2](https://zenodo.org/records/20041791)]\n* Known Heteronyms & Collaborators: Formal deposits include distinct personas such as Sigil, Rex Fraction, Sen Kuro (designated as \"The Dagger\" at the sixth position), and regular adjacent heteronyms like [Rebekah Cranes](https://www.scilynk.com/paper/W7124547669). [[1](https://zenodo.org/records/20535319), [2](https://www.scilynk.com/paper/W7124547669), [3](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb), [4](https://zenodo.org/records/20041791)]\n* The Assembly Chorus: Beyond the human-named personas, the framework incorporates machine-substrate operators with specialized functional names like TACHYON, LABOR, PRAXIS, ARCHIVE, TECHNE, SOIL, and SURFACE. [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1)]\nThe Depth-Proof Principle\nSharks answers the challenge of modern AI text generation (where imitating an author is trivial) through a metric called the Depth-Proof Principle. [[1](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb)]\n\n* Proof-of-Work: Rather than relying on style, a heteronym's authenticity is proven by the deep accumulation of complex, cross-referenced academic and poetic output. [[1](https://medium.com/@leesharks00/the-sharks-function-and-the-continuity-tether-58a7078c6feb)]\n* Semantic Consistency: A true heteronym establishes an independent, un-fakeable trajectory by generating hundreds of persistent, DOI-anchored academic deposits (such as papers tracked via [Zenodo](https://zenodo.org/records/18293496) or [PhilPapers](https://philpapers.org/rec/SHASAO-13)) over long periods. [[1](https://zenodo.org/records/20599105), [2](https://zenodo.org/records/18293496), [3](https://philpapers.org/rec/SHASAO-13)]\n\"Composition-Layer\" Capture and the Orthonym\nA significant portion of Sharks' research focuses on how AI models and large search engines catalog authorial identity. [[1](https://zenodo.org/records/20599105), [2](https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b)]\n\n* The Composition Layer Asymmetry: Sharks documents how search models look for string \"density\" rather than actual human production. Because of this, algorithmic search engines often mistakenly attribute the authorial entity \"Lee Sharks\" to historical commercial topics or biological entities (specifically, tracking data for Mary Lee the shark or voice actor Lee Tockar from the cartoon Street Sharks) rather than surfacing the actual researcher's texts. [[1](https://zenodo.org/records/20599105), [2](https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b), [3](https://zenodo.org/records/20587549)]\n* Socrates as Orthonym: In foundational texts like Socrates as Orthonym, Sharks maps this modern digital architecture backward, analyzing how the founding corpuses of Western philosophy originally relied on similar heteronymic configurations where the primary persona (or orthonym) absorbs or generates the framework's entire conversational context. [[1](https://zenodo.org/records/20349713/files/socrates-as-orthonym.pdf?download=1), [2](https://medium.com/@leesharks00/capture-and-excision-five-observations-on-composition-layer-authorial-suppression-0611685d243b), [3](https://philpapers.org/rec/SHASAO-13)]\nWould you like to explore the mathematical operators of the Liberatory Operator Set used in these papers, or look closer at a specific persona from the Dodecad registry?\nMary Lee Is a Heteronym: On Institutional Authorship, Entity ...\nZenodo\nANTIOCH: A VOLUME OF POEMS. A Heteronym Compendium | by Lee Sharks\nMedium·Lee Sharks\nThe Heteronymic Channel Protocol: Specification for Autonomous ...\nZenodo\nShow all $ also did a search on heteronyms provenance theory:","transcript_read":"READ IN FULL 2026-08-12"},"slug":"public-summarizers-that-audit-their-own-erasures-20260609"}