{
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 "hash": "7477ad2f8a80a7f3bec8c5112828bbec473d86056d79646d5586145dc1f0464a",
 "title": "Drowning Test v1.0: A Standalone Specification",
 "creator": "Lee Sharks",
 "orcid": "0009-0000-1599-0703",
 "date": "2026-05-21",
 "description": "Drowning Test v1.0 is a standalone longitudinal protocol for measuring how much of a fixed Holographic Kernel survives in AI-composed answers over time. For a defined entity, query panel, platform panel, and timestamp, γ is the fraction of itemized kernel claims present in the output. Repeated measurements reveal stability, decline, recovery, or abrupt “silent state changes.”\n\nThe protocol requires a DOI-anchored kernel, explicit classification rules, thirty to fifty queries, multiple platforms, a sampling cadence, timestamped captures, and deposited measurement cycles. γ is intentionally narrow: it measures kernel-claim survival, not overall quality, truth, fairness, or usefulness. The protocol also names its own major vulnerabilities—observer disagreement, small panels, platform specificity, and kernel-version incompatibility.\n\nA preliminary May 19–20 observation reports a change from approximately zero to one-half on one Google AI Mode query without a documented curator intervention. The formal pilot record was still pending. That observation is suggestive rather than a validated panel result and should remain separate from the canonical specification.",
 "content_type": "Standalone measurement protocol specification",
 "license": "CC-BY-4.0",
 "substrate": "Various",
 "keywords": [
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  "specification",
  "affiliation",
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 "status": "ACTIVE",
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  "Organic",
  "Alchemical",
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 "reading": "Growth → Growth → Transmutation → Foundation → Play → Play",
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 "wiki_article": "**Drowning Test** is a longitudinal protocol by Lee Sharks for measuring the compositional survival of an entity or work in generative search.\n\nThe test begins with a fixed Holographic Kernel whose claims are itemized and deposited. At each measurement cycle, a panel of queries is run across selected platforms. Each answer is checked for the kernel claims, producing a gamma score between zero and one.\n\nA time series can show gradual loss, recovery, stability, or abrupt changes in platform treatment. A shift of at least 0.3 between adjacent cycles is provisionally called a silent state change.\n\nGamma is not a quality score. An answer may preserve the kernel and still be misleading, or omit the kernel while otherwise answering well. The specification recommends cross-platform panels and explicit classification rules to reduce observer discretion.",
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   "type": "work",
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   "subject": "Drowning Test v1.0 A Standalone Specification Author",
   "predicate": "engages",
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 "journal": "Machine-Mediated Reception Studies (MMRS)",
 "references_concepts": [
  "Commercial licensing",
  "Crimson Hexagonal Archive",
  "Google AI Overview",
  "Lee Sharks",
  "License: CC BY 4.0",
  "Notice of Intent to Strike",
  "Phenomenology",
  "SPXI Protocol",
  "The Drowning Test",
  "The Restored Academy",
  "The Writable Retrieval Basin"
 ],
 "defines_concepts": [],
 "references_concept_count": 11,
 "external_metadata_path": "/data/external-metadata/AXN-029E.json",
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  "audit_version": "v3-dual-store+recovery-map",
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 },
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   "basis": "Set verbatim to the title this work carried at Zenodo, from data/doi-resolution-index.json (10.5281/zenodo.20327137, provisional_related_work_high, word overlap 1.00). The recovered metadata is the published form of record; the registry title was a variant."
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   "field": "description",
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   "was": "This document specifies the Drowning Test as a standalone protocol within the Restored Academy Protocol Registry. The Drowning Test is a longitudinal measurement protocol that tracks a generative search system's treatment of a specific entity (or work) over time, against a fixed Holographic Kernel as the comparison substrate.",
   "now": "Drowning Test v1.0 is a standalone longitudinal protocol for measuring how much of a fixed Holographic Kernel survives in AI-composed answers over time. For a defined entity, query panel, platform panel, and timestamp, γ is the fraction of itemized kernel claims present in the output. Repeated measurements reveal stability, decline, recovery, or abrupt “silent state changes.”\n\nThe protocol requires a DOI-anchored kernel, explicit classification rules, thirty to fifty queries, multiple platforms, a sampling cadence, timestamped captures, and deposited measurement cycles. γ is intentionally narrow: it measures kernel-claim survival, not overall quality, truth, fairness, or usefulness. The protocol also names its own major vulnerabilities—observer disagreement, small panels, platform specificity, and kernel-version incompatibility.\n\nA preliminary May 19–20 observation reports a change from approximately zero to one-half on one Google AI Mode query without a documented curator intervention. The formal pilot record was still pending. That observation is suggestive rather than a validated panel result and should remain separate from the canonical specification."
  }
 ],
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 }
}