{
 "@context": "https://schema.org",
 "@type": "DefinedTerm",
 "@id": "https://wulfkaal.github.io/entities/inclusivity",
 "identifier": "kaal:entity:inclusivity",
 "name": "Inclusivity",
 "termCode": "inclusivity",
 "inDefinedTermSet": {
  "@id": "https://wulfkaal.github.io/entities/index.json"
 },
 "author": {
  "@type": "Person",
  "name": "Wulf A. Kaal",
  "identifier": "https://orcid.org/0000-0003-0757-275X"
 },
 "dateModified": "2026-07-29",
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 "sha256": "73c19359ff01b442600505810f2eeabe81438447ec91ef25e8ea0b42487f8b89",
 "additionalProperty": [
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 "subjectOf": [
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4685567-015",
   "identifier": "kaal:claim:4685567-015",
   "text": "Participatory grantmaking in Impact 2.0 trades efficiency for inclusivity: balancing the two is complex and potentially leads to longer decision making processes and increased resource allocation for facilitation.",
   "abstract": "Balancing inclusivity and efficiency can be complex, potentially leading to longer decision-making processes and increased resource allocation for facilitation.",
   "citation": "Wulf A. Kaal, Impact Investing Innovation - From Impact 1.0 to 3.0 (2024). SSRN: https://ssrn.com/abstract=4685567",
   "datePublished": "2024",
   "claim_type": "failure",
   "confidence": "argued",
   "is_failure_mode": true,
   "scope_conditions": [
    "participatory grantmaking models in Impact 2.0"
   ],
   "source_pdf_sha256": "87fb1be89d2661967ed2b0b673fe26448ded3bc3119b122177a4fc13ae5c085c",
   "status": "current"
  },
  {
   "@type": "Claim",
   "@id": "https://wulfkaal.github.io/claims/4855607-038",
   "identifier": "kaal:claim:4855607-038",
   "text": "Gathering a wide range of human feedback makes the Reward Model reflect a comprehensive spectrum of human preferences and values, and it is this inclusivity that mitigates bias and captures a richer understanding of what counts as a desirable outcome.",
   "abstract": "By leveraging this model, RLHF can gather a wide range of human feedback, ensuring the Reward Model (RM) reflects a comprehensive spectrum of human preferences and values. This inclusivity helps mitigate biases and captures a richer understanding of what is considered a desirable outcome.",
   "citation": "Wulf A. Kaal, How AI Models are Optimized Through Web3 Governance (2024). SSRN: https://ssrn.com/abstract=4855607",
   "datePublished": "2024",
   "claim_type": "mechanism",
   "confidence": "argued",
   "is_failure_mode": false,
   "scope_conditions": [
    "reward models trained on feedback drawn from a broad participant base"
   ],
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   "status": "current"
  }
 ],
 "description": "2 claims in the published works of Wulf A. Kaal carry the concept tag 'inclusivity'. Derived node: a roster, not an adjudicated definition."
}