{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/trace-the-claim-to-an-accountable-origin-before-counting-citations",
  "id": "MHC-D-RESEARCH-0424",
  "version": "0.1.0",
  "title": "Trace the claim to an accountable origin before counting citations",
  "summary": "Ten echoes can still have one source.",
  "kind": "protocol",
  "body": "Follow citations, links or quoted data backward until you reach the earliest accountable source you can inspect: primary data, original study, official record or named first-hand document. Record which later sources are independent and which merely repeat that origin. Count evidence paths, not web pages.",
  "limits": [
    "The earliest source is not automatically the best or correct source; inspect methods, scope and later corrections too."
  ],
  "topics": [
    "union-provenance-verification"
  ],
  "intents": [],
  "source_ids": [
    "RS-FF258DEA21EDA7AB"
  ],
  "evidence": [
    {
      "claim": "The provenance-neglect framework argues that verification systems can create circular reasoning and synthetic-content amplification when they treat source quantity as a proxy for source quality.",
      "source_id": "RS-FF258DEA21EDA7AB",
      "role": "supports",
      "note": "This is a conceptual and prescriptive analysis rather than a quantified causal estimate across all AI systems.",
      "locator": "Provenance neglect as systematic vulnerability"
    },
    {
      "claim": "The 2026 framework recommends weighting verification evidence by traceable origin, source credibility and institutional accountability rather than treating all retrieved sources as equally reliable.",
      "source_id": "RS-FF258DEA21EDA7AB",
      "role": "supports",
      "note": "Credibility signals can themselves be imperfect and should not become a permanent whitelist that blocks contrary evidence.",
      "locator": "Provenance-Weighted Authenticity Verification framework"
    }
  ],
  "use_when": [
    "Many articles, AI answers or posts repeat the same claim and repetition is starting to look like corroboration."
  ],
  "avoid_when": [
    "The earliest source is not automatically the best or correct source; inspect methods, scope and later corrections too."
  ],
  "example": "Five AI-generated summaries may all cite blog posts that ultimately refer to the same vendor benchmark; treat that as one evidence lineage.",
  "check": "The evidence map shows independent origins rather than a raw count of repeating sources.",
  "steps": [
    "Extract the exact material claim.",
    "Follow each supporting citation or link backward.",
    "Identify the earliest inspectable accountable origin.",
    "Group downstream sources that depend on the same origin.",
    "Look separately for genuinely independent evidence or contradiction."
  ],
  "sources": [
    {
      "id": "RS-FF258DEA21EDA7AB",
      "title": "The Original Sins of Algorithmic Authenticity Verification: Provenance Neglect and Model Collapse in AI-Mediated News and Information",
      "url": "https://www.tandfonline.com/doi/full/10.1080/08838151.2026.2691175"
    }
  ],
  "relations": [
    {
      "from": "MHC-D-RESEARCH-0424",
      "to": "MHC-D-RESEARCH-0425",
      "type": "use_before",
      "url": "/knowledge/weight-evidence-by-traceability-not-popularity"
    }
  ],
  "collections": [
    {
      "id": "RC-3F875FF96207B5CE",
      "title": "Trace where information came from before deciding whether to trust it",
      "url": "/collections/trace-where-information-came-from-before-deciding-whether-to-trust-it"
    }
  ]
}