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 "name": "Streaming Where Are The Ai Governance Roles An Early Stage Empirical Mapping Of Pr 6502785E23",
 "text": "Where Are the AI Governance Roles? An Early-Stage Empirical Mapping of Presence, Absence, and Structure in Organisational AI Oversight presents the following source proposition: Empirical evidence on how organisations truly govern AI—and where responsibility is fundamentally lacking—remains scarce. This proposition is pertinent to Kaal's source-bound claim that Deep reinforcement learning demands large amounts of training data, which suggests its algorithms differ fundamentally from human learning, and learning without supervision becomes particularly hard when rewards are sparse, as they typically are in sequence generation tasks. The proposed response is an agreement: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.",
 "author": {
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.356.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
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  "url": "https://doi.org/10.20944/preprints202602.1200.v1"
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  "url": "https://wulfkaal.github.io/claims/4855607-014",
  "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
  "paper": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance",
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 "userAffirmation": "I affirm batch kaal-review:2026-07-31:streaming-etl-0008, SHA-256 469be3af77a43b8a86f52c2e8253882abdefc7a0da53c392329c3cb8a11e8160, as written and authorize publication of all 250 response claims on my canonical property, preserving their evidence levels, ambiguity labels, provenance, and the unchanged 5,033 scholarly claims.",
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