{
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  "permalink": "https://www.isonomia.app/a/4Tjvzqia",
  "version": 1,
  "contentHash": "sha256:33bb916ba176d8035cd046e27271e7dd2e8aa0086d2e98f49179569944684e7c",
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  "isoId": "iso:argument:4Tjvzqia",
  "isoUrl": "https://www.isonomia.app/iso/argument/4Tjvzqia",
  "doi": null,
  "retrievedAt": "2026-08-24T19:50:08.742Z",
  "createdAt": "2026-05-04T21:17:36.121Z",
  "updatedAt": "2026-05-04T21:17:36.121Z",
  "conclusion": {
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    "moid": "e3b90417472b6624a4b908a44d84dcbfb1645457a1360f48889864c351f7171f",
    "text": "Selection bias in volunteer samples would more plausibly bias toward finding effects rather than null effects, because volunteers willing to modify behavior may be more engaged users with greater exposure to algorithmic content."
  },
  "premises": [
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      "moid": "94b87747ae057def38a460b9711c3320afd9733659ef4814294b3fb09183579b",
      "text": "The Allcott et al. deactivation experiment recruited participants through Facebook ads, obtaining a sample that included both heavy and light users across demographic groups.",
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      "text": "If volunteers were systematically less susceptible to algorithmic influence, the 2023 Meta experiments' null findings would be conservative estimates, not overestimates, of population effects.",
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        "cqKey": "alternative_causes",
        "text": "Could a different cause produce the same effect E in this case?",
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