{
  "paper": {
    "num": 22,
    "title": "Damned if You Do(n't): How Epistemic Technology Spoils Expertise",
    "authors": "Ayman Ajaj, Mayur Joshi, Mathieu Bouchard",
    "affiliation": "University of Ottawa"
  },
  "inputs": {
    "case": "radiologist",
    "profession": "Radiologist at a community hospital",
    "statements": 4,
    "corpus": "36 comments (default)",
    "judge A": "claude-haiku-4.5",
    "judge B": "claude-sonnet-4.6"
  },
  "tiles": [
    {
      "label": "Your case",
      "value": "Shunned expertise",
      "sub": "Radiologist at a community hospital; adoption, others \u00b7 second judge: Slopped (disagree) \u00b7 5/5 quotes verified verbatim",
      "tone": "accent"
    },
    {
      "label": "Damned if you don't",
      "value": "Outdated",
      "sub": "the quadrant the same audience points to had you made the opposite adoption choice",
      "tone": "warn"
    },
    {
      "label": "Two-judge agreement (\u03ba)",
      "value": "0.93",
      "sub": "95% CI [0.82, 1.00] on 36 comments \u00b7 stance \u03ba 0.89, locus \u03ba 0.96",
      "tone": "good"
    },
    {
      "label": "Most common form (both judges agree)",
      "value": "Shunned",
      "sub": "8 of 36 comments \u00b7 default corpus",
      "tone": "neutral"
    }
  ],
  "figures": [
    {
      "path": "fig_quadrant.png",
      "title": "Where does your situation sit in the 2\u00d72?",
      "caption": "\u2605 = the overall reading of your case. Dots = each of your statements coded separately by the two judges. The dashed arrow points to the mirror quadrant: the 'damned if you don't' alternative."
    },
    {
      "path": "fig_corpus.png",
      "title": "Can the 2\u00d72 be coded reliably?",
      "caption": "Two LLM judges (different models, same codebook: the paper's definitions) coded every comment independently. Counts reflect the corpus as written, not how common each form is in the world."
    },
    {
      "path": "fig_confusion.png",
      "title": "Where do the judges disagree?",
      "caption": "Rows = judge A, columns = judge B. Off-diagonal cells are the ambiguous cases worth reading."
    }
  ],
  "compare": [
    {
      "metric": "Slopped expertise (adoption, self)",
      "paper": "This form captures experts who adopt epistemic technology and see their own expertise devalued by such adoption. Experts internalize the discourse tha\u2026",
      "yours": "A 7 \u00b7 B 7 \u00b7 agreed 7",
      "note": "corpus counts"
    },
    {
      "metric": "Lagging expertise (non-adoption, self)",
      "paper": "This form captures experts who do not adopt epistemic technologies, but who internalize societal discourse that valorizes the capabilities and outputs\u2026",
      "yours": "A 5 \u00b7 B 5 \u00b7 agreed 5",
      "note": "corpus counts"
    },
    {
      "metric": "Shunned expertise (adoption, others)",
      "paper": "This form captures experts who adopt epistemic technologies, and are stigmatized by others for doing so. This adoption is perceived by the expert's au\u2026",
      "yours": "A 8 \u00b7 B 8 \u00b7 agreed 8",
      "note": "corpus counts"
    },
    {
      "metric": "Outdated expertise (non-adoption, others)",
      "paper": "This form illustrates experts who do not adopt epistemic technologies and are stigmatized by others for this resistance. These non-adopters become sym\u2026",
      "yours": "A 9 \u00b7 B 7 \u00b7 agreed 7",
      "note": "corpus counts"
    },
    {
      "metric": "Transitions",
      "paper": "Slopped \u2192 Shunned; Outdated \u2192 Slopped (\u00a74)",
      "yours": "Shunned \u2192 Slopped (extrapolation (relabelled: not a transition the paper describes)); Shunned \u2192 Outdated (extrapolation)",
      "note": "'extrapolation' = not in the paper"
    },
    {
      "metric": "Empirical measurement",
      "paper": "none (conceptual paper; proposes an empirical research agenda)",
      "yours": "\u03ba = 0.93 between two LLM coders",
      "note": "codability, not prevalence"
    },
    {
      "metric": "Agreement: clear-cut vs deliberately ambiguous comments",
      "paper": "\u2014",
      "yours": "97% vs 83%",
      "note": "30 vs 6 comments"
    },
    {
      "metric": "Judges vs the corpus author's intended labels",
      "paper": "\u2014",
      "yours": "\u03ba A 0.96 \u00b7 B 0.96",
      "note": "synthetic corpus"
    }
  ],
  "runbook": {
    "name": "smash-22-spoiled-expertise",
    "url": ""
  }
}