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 "text": "Governance Architecture for AI-Mediated Safeguarding presents the following source proposition: In most cases, organisations developing AI systems simultaneously define safety standards, evaluate compliance internally, and publicly communicate their own safety claims. This proposition is pertinent to Kaal's source-bound claim that WDAGs allow new regulatory and ethical standards to be integrated into existing AI systems without overhauling the entire model architecture, which is what makes rapid legal adaptation feasible in sectors such as public safety and healthcare. The proposed response is an extension: 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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  "identifier": "https://orcid.org/0009-0008-7840-1847"
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  "External evidence level: abstract indexed.",
  "Mapping review tier: moderate-confidence claim review.",
  "Primary mapping confidence: 0.4219.",
  "The source-to-claim mapping remains explicitly ambiguous and is published with that limitation."
 ],
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  "url": "https://doi.org/10.2139/ssrn.6737799"
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  "url": "https://wulfkaal.github.io/claims/4855607-024",
  "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",
  "authors": [
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  "year": "2024",
  "ssrn": "https://ssrn.com/abstract=4855607",
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  "workAuthors": [
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    "whyRelevant": "Shared high-information concepts: simultaneously, standards, compliance. Scope: SEC implementation and clarification of Title IV registration and reporting rules.",
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