{
  "identifier": "01AGjakA",
  "argumentId": "cmowh5zmg011p8ch6epwvlu0d",
  "permalink": "https://www.isonomia.app/a/01AGjakA",
  "version": 1,
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  "isoId": "iso:argument:01AGjakA",
  "isoUrl": "https://www.isonomia.app/iso/argument/01AGjakA",
  "doi": null,
  "retrievedAt": "2026-07-16T18:21:06.438Z",
  "createdAt": "2026-05-08T05:28:56.728Z",
  "updatedAt": "2026-05-08T05:28:56.728Z",
  "conclusion": {
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    "moid": "52b1e442f01699bff01de7acb924e95f8d069e8cbe95f62f010b7bddd72ce7e6",
    "text": "Guess et al. (2023) null findings do not rule out algorithmic effects below the study's minimum detectable effect size or effects on non-volunteer subpopulations with different baseline polarization levels."
  },
  "premises": [
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      "moid": "68949072bc9eb443efec9bc3012c4c46fa9e1ad52ce14341d4a4730e34a998a4",
      "text": "Absence-of-evidence arguments require specification of the minimum detectable effect size and acknowledgment that non-detection does not prove absence, particularly when volunteer bias may suppress effects.",
      "isImplicit": false
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      "moid": "49906eab6387d8a25160ce554ec242cc27373b26e0489d215457e45ded4aca5c",
      "text": "Guess et al. (2023) conducted a large field experiment with consenting volunteers, introducing volunteer bias that may reduce or mask treatment effects relative to the full population.",
      "isImplicit": false
    },
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      "text": "The framing's ≥10% bar requires evidence that effects do not reach that threshold; null results in volunteer samples do not establish this across the full population.",
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    }
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    "name": "Argument from Lack of Evidence (Negative Evidence)",
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        "cqKey": "absence_of_proof_vs_proof_of_absence?",
        "text": "Is the absence of positive evidence strong enough to justify concluding ¬A, or only to justify withholding belief in A?",
        "attackKind": "UNDERCUTS",
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