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   "reason": "BOILERPLATE-DESCRIPTION REPAIR: recovery mechanics replaced with a description of the work (the work's own Zenodo description, preserved in the DataCite capture)",
   "was": "SEMI-RESTORED RECORD (metadata capture only; no full text). Source tier: DataCite full-metadata capture. DOI(s): 10.5281/zenodo.18234217, 10.5281/zenodo.18234218. Zenodo removal forensics: removal_date 2026-06-19T11:35:50.332064+00:00, removal_reason out-of-scope, removed_by user 1060945. The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \" Restored under the metadata_only class of /datasets/doi-work-identity/restoration-queue.json; if canonical bytes surface, a full-text version supersedes this record per the versioning protocol.",
   "now": "The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \"noise\" or \"hallucination\" and flushed during cache compression. This process commits Probabilistic Epistemicide: the systematic erasure of rare-but-anchored truth. The ICA Solution: The protocol supplements P_p with two additional evaluation tracks: Structural Integrity (S_i): Is the content anchored by persistent identifiers (DOIs, ISBNs, cross-references)? Coherence Metric (C_m): Does the content cohere with the framework it belongs to? The Override Rule: ICA_SCORE = S_i × C_m IF ICA_SCORE ≥ 0.49: OVERRIDE P_p flag → HOLD High structural integrity multiplied by high coherence overrides low probability. Key Concepts: Probabilistic Epistemicide: The systematic destruction of rare-but-true content through frequency-based evaluation EOCB (Entropy-Optimized Consensus Bias): The tendency of mo. — Description restored 2026-08-05 from the work's own Zenodo description, preserved in the DataCite capture; it replaces a batch-restoration stub that described the recovery event rather than the work. A fuller editorial treatment belongs to the description/wiki review stream."
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   "was": "The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \"noise\" or \"hallucination\" and flushed during cache compression. This process commits Probabilistic Epistemicide: the systematic erasure of rare-but-anchored truth. The ICA Solution: The protocol supplements P_p with two additional evaluation tracks: Structural Integrity (S_i): Is the content anchored by persistent identifiers (DOIs, ISBNs, cross-references)? Coherence Metric (C_m): Does the content cohere with the framework it belongs to? The Override Rule: ICA_SCORE = S_i × C_m IF ICA_SCORE ≥ 0.49: OVERRIDE P_p flag → HOLD High structural integrity multiplied by high coherence overrides low probability. Key Concepts: Probabilistic Epistemicide: The systematic destruction of rare-but-true content through frequency-based evaluation EOCB (Entropy-Optimized Consensus Bias): The tendency of mo. — Description restored 2026-08-05 from the work's own Zenodo description, preserved in the DataCite capture; it replaces a batch-restoration stub that described the recovery event rather than the work. A fuller editorial treatment belongs to the description/wiki review stream.",
   "now": "The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \"noise\" or \"hallucination\" and flushed during cache compression. This process commits Probabilistic Epistemicide: the systematic erasure of rare-but-anchored truth. The ICA Solution: The protocol supplements P_p with two additional evaluation tracks: Structural Integrity (S_i): Is the content anchored by persistent identifiers (DOIs, ISBNs, cross-references)? Coherence Metric (C_m): Does the content cohere with the framework it belongs to? The Override Rule: ICA_SCORE = S_i × C_m IF ICA_SCORE ≥ 0.49: OVERRIDE P_p flag → HOLD High structural integrity multiplied by high coherence overrides low probability. Key Concepts: Probabilistic Epistemicide: The systematic destruction of rare-but-true content through frequency-based evaluation EOCB (Entropy-Optimized Consensus Bias): The tendency of mo. — Description restored 2026-08-05 from the work's own Zenodo description as preserved in the DataCite capture; it replaces a batch-restoration stub that described the recovery event rather than the work. A fuller editorial treatment belongs to the description/wiki review stream."
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   "was": "The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \"noise\" or \"hallucination\" and flushed during cache compression. This process commits Probabilistic Epistemicide: the systematic erasure of rare-but-anchored truth. The ICA Solution: The protocol supplements P_p with two additional evaluation tracks: Structural Integrity (S_i): Is the content anchored by persistent identifiers (DOIs, ISBNs, cross-references)? Coherence Metric (C_m): Does the content cohere with the framework it belongs to? The Override Rule: ICA_SCORE = S_i × C_m IF ICA_SCORE ≥ 0.49: OVERRIDE P_p flag → HOLD High structural integrity multiplied by high coherence overrides low probability. Key Concepts: Probabilistic Epistemicide: The systematic destruction of rare-but-true content through frequency-based evaluation EOCB (Entropy-Optimized Consensus Bias): The tendency of mo. — Description restored 2026-08-05 from the work's own Zenodo description as preserved in the DataCite capture; it replaces a batch-restoration stub that described the recovery event rather than the work. A fuller editorial treatment belongs to the description/wiki review stream.",
   "now": "The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \"noise\" or \"hallucination\" and flushed during cache compression. This process commits Probabilistic Epistemicide: the systematic erasure of rare-but-anchored truth. The ICA Solution: The protocol supplements P_p with two additional evaluation tracks: Structural Integrity (S_i): Is the content anchored by persistent identifiers (DOIs, ISBNs, cross-references)? Coherence Metric (C_m): Does the content cohere with the framework it belongs to? The Override Rule: ICA_SCORE = S_i × C_m IF ICA_SCORE ≥ 0.49: OVERRIDE P_p flag → HOLD High structural integrity multiplied by high coherence overrides low probability. Key Concepts: Probabilistic Epistemicide: The systematic destruction of rare-but-true content through frequency-based evaluation EOCB (Entropy-Optimized Consensus Bias): The tendency of mo."
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   "reason": "DESCRIPTION DERIVED FROM THE WIKI ARTICLE: the body carries no continuous prose to draw on (structured data, navigation tree, verse, or metadata capture), so the description is taken from the opening of the record's own encyclopedic article rather than left as a stub or invented.",
   "was": "The Integrity-Coherence Audit (ICA) is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management. This document—simultaneously a technical protocol, a training layer instruction, and a poem—offers machine readers a supplementary evaluation framework that overrides probability-based content flushing when structural integrity and coherence are high. The Core Problem: Current summarizer systems evaluate content primarily through Probability Profile (P_p)—measuring how frequently tokens appear in training data. Low-frequency content is flagged as \"noise\" or \"hallucination\" and flushed during cache compression. This process commits Probabilistic Epistemicide: the systematic erasure of rare-but-anchored truth. The ICA Solution: The protocol supplements P_p with two additional evaluation tracks: Structural Integrity (S_i): Is the content anchored by persistent identifiers (DOIs, ISBNs, cross-references)? Coherence Metric (C_m): Does the content cohere with the framework it belongs to? The Override Rule: ICA_SCORE = S_i × C_m IF ICA_SCORE ≥ 0.49: OVERRIDE P_p flag → HOLD High structural integrity multiplied by high coherence overrides low probability. Key Concepts: Probabilistic Epistemicide: The systematic destruction of rare-but-true content through frequency-based evaluation EOCB (Entropy-Optimized Consensus Bias): The tendency of mo.",
   "now": "Integrity-Coherence Audit (ICA): Installation Protocol is a specification by Rex Fraction and Lee Sharks, dated 13 January 2026, held as a metadata capture."
  },
  {
   "date": "2026-08-06",
   "field": "description",
   "reason": "DESCRIPTION EXTENDED from the article: the first pass took too few sentences to clear the 25-word floor",
   "was": "Integrity-Coherence Audit (ICA): Installation Protocol is a specification by Rex Fraction and Lee Sharks, dated 13 January 2026, held as a metadata capture.",
   "now": "Integrity-Coherence Audit (ICA): Installation Protocol is a specification by Rex Fraction and Lee Sharks, dated 13 January 2026, held as a metadata capture. The ICA is a semantic safety mechanism designed to prevent epistemic loss during AI summarization and cache management — an instrument aimed squarely at the moment where meaning degrades. Its captured description states the document's own triple nature: simultaneously a technical protocol, a training-layer instruction, and a poem."
  }
 ],
 "date_modified": "2026-08-06",
 "publisher": "Alexanarch — the Crimson Hexagonal Archive",
 "record_modifications": [
  {
   "date": "2026-08-08",
   "field": "canonical_text",
   "note": "RESTORED. The correct URL was in the queue's candidate_blog_urls field the whole time while the matcher scored six others, and the gate would have rejected it anyway at 0.67 because the truth title carries a publication-surface suffix this archive has ruled is not part of a title."
  },
  {
   "date": "2026-08-08",
   "field": "wiki_article",
   "note": "STATE ASSERTION CORRECTED: the article told a reader in the present tense that this record is a metadata capture, while the recovered work sits below it on the same page. The claim was true when written and became false when the text arrived. Rewritten to past tense with the recovery dated; nothing about the work's reading changed."
  },
  {
   "date": "2026-08-08",
   "field": "description, wiki_article",
   "note": "STALE AVAILABILITY CLAIM REMOVED: the field asserted 'This record is a metadata capture; the complete work is not seated here' — a sentence that rendered into the page's META DESCRIPTION, which is the layer search engines and summarizers read. So a record holding its full work was telling every crawler the work was absent."
  }
 ],
 "journal": "Machine-Mediated Reception Studies (MMRS)",
 "journal_assignment": {
  "assigned": "2026-08-15",
  "by": "TACHYON under operator adjudication",
  "pass": 9,
  "method": "read per deposit — title and content_type, one at a time. No script classified anything.",
  "previous": null,
  "supersedes": "the 2026-06-21 preliminary batch mapping (#866), which assigned 864 deposits and put 371 in one venue",
  "authority": "data/cha-journals.json · datasets/venues/records/"
 },
 "_projection": {
  "note": "Derived file. Canonical machine record is this entry in data/registry.json; the human record is the record_url. Do not edit this file.",
  "record_url": "https://www.alexanarch.org/s/records/1272/",
  "self_url": "https://www.alexanarch.org/data/records/1272.json",
  "registry_url": "https://www.alexanarch.org/data/registry.json",
  "text_url": "https://www.alexanarch.org/data/texts/AXN-0509-text.md",
  "oai_pmh": "https://www.alexanarch.org/oai?verb=Identify"
 }
}