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 "text": "Minimizing Data Exposure in Higher Education LLM Applications: Evaluating the Model Context Protocol (MCP) for Preserving Privacy in Academic Advising should be assessed against Kaal's source-bound claim that The move by AI developers toward smaller training datasets raises the risk of overfitting, especially with complex models, which forces LLM developers to rely on regularization to counteract overfitting of the model to the training data. The current metadata indicates a plausible connection through model context protocol, but the defensible response is a qualification until the source text confirms agreement, scope, methods, and limitations.",
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  "Mapping review tier: ambiguity triage before claim review.",
  "The literature-to-claim mapping remains explicitly ambiguous and should not be treated as a settled equivalence."
 ],
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  "citation": "Wulf A. Kaal, AI Learning - Decentralized Governance to Optimize Human Output Datasets for AI Learning (2024). SSRN: https://ssrn.com/abstract=4755632",
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