{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/reputation-feedback",
 "identifier": "kaal:entity:reputation-feedback",
 "name": "Reputation feedback",
 "termCode": "reputation-feedback",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0009-0008-7840-1847"
 },
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   "value": "derived"
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   "value": 2
  },
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   "name": "work_count",
   "value": 2
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   "name": "year_span",
   "value": [
    "2021",
    "2026"
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 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/3962614-033",
   "identifier": "kaal:claim:3962614-033",
   "text": "Through the decentralized governance precedent system, projects upvoted by the whale validation pool that have the highest comparative ROI get more citations in the reputation system and continually enhance the reputation of the whale who sourced and proposed them.",
   "abstract": "Through the decentralized governance precedent system, projects that were upvoted by the Whale validation pool and have the highest comparative ROI get more citations in the reputation system and continually enhance the reputation of the whale who sourced and proposed it.",
   "citation": "Wulf A. Kaal, REPUTATION AS CAPITAL – How Decentralized Autonomous Organizations Address Shortcomings in the Ventu (2021). SSRN: https://ssrn.com/abstract=3962614",
   "datePublished": "2021",
   "claim_type": "mechanism",
   "confidence": "asserted",
   "is_failure_mode": false,
   "scope_conditions": [
    "a DAO operating a decentralized governance precedent system"
   ],
   "source_pdf_sha256": "c6e202e77fd08db801f932774026458b94e5d103abf30cb1189fe6a1c5edf391",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/7598518-002",
   "identifier": "kaal:claim:7598518-002",
   "text": "The paper follows on from the author's Reinforcement Learning from Reputation Feedback paper and tests one use of that mechanism, as a filter on training data, and not the mechanism as a whole.",
   "abstract": "Intelligence models increasingly learn from data that earlier Intelligence models produced, and when that loop repeats the rare cases disappear first. This paper preregisters a test of whether a reputation-weighted validator pool, filtering the training data of a recursive replacement loop, keeps those rare cases alive. The outcomes remain sealed until a replication of the original model-collapse result succeeds.",
   "citation": "Wulf A. Kaal, Can Reputation Prevent Model Collapse? A Preregistered Test of Reputation-Weighted Data Filtering (2026). SSRN: https://ssrn.com/abstract=7598518",
   "datePublished": "2026",
   "claim_type": "condition",
   "confidence": "stated",
   "is_failure_mode": false,
   "scope_conditions": [
    "Scope is relative to the author's parent reputation-feedback paper.",
    "The restriction is the paper's own statement of what it does and does not test."
   ],
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   "status": "current"
  }
 ],
 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'reputation-feedback'. Derived node: a roster, not an adjudicated definition."
}