curated_examples.json

[
  {
    "title": "Indirect abuse: baseline misses, A and B catch",
    "language": "English",
    "text": "I didn't know the right wing loved islam",
    "true_label": "harmful",
    "sarcasm_score": "0.75",
    "source": "cad",
    "models": [
      {
        "name": "Baseline",
        "prob": "0.45",
        "decision": "non-harmful (MISS)"
      },
      {
        "name": "Mechanism A",
        "prob": "--",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism B",
        "prob": "0.61",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism D",
        "prob": "0.60",
        "decision": "HARMFUL"
      }
    ],
    "story": "CAD is where the mechanisms' recall gains concentrate (baseline recall 0.389 -> B 0.593, p<1e-5): contextual, indirect abuse benefits most from the auxiliary non-literality signal."
  },
  {
    "title": "Tunisian dialect: the worry that turned out backwards",
    "language": "Arabic",
    "text": "اتقوا انفسكم ياتوانسه",
    "true_label": "harmful",
    "sarcasm_score": "0.21",
    "source": "thsab",
    "models": [
      {
        "name": "Baseline",
        "prob": "0.26",
        "decision": "non-harmful (MISS)"
      },
      {
        "name": "Mechanism A",
        "prob": "--",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism B",
        "prob": "0.80",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism D",
        "prob": "0.18",
        "decision": "non-harmful (MISS)"
      }
    ],
    "story": "Despite zero Tunisian data in the sarcasm layer's training pool, A and B are significantly effective on T-HSAB specifically (McNemar p=0.015 / p=0.029) -- the suspected weak point was verified to be a strength."
  },
  {
    "title": "Sarcastic camouflage: high sarcasm score, hostile intent",
    "language": "Arabic",
    "text": "يلعن صباحك نزعتلي نهاري",
    "true_label": "harmful",
    "sarcasm_score": "0.85",
    "source": "lhsab",
    "models": [
      {
        "name": "Baseline",
        "prob": "0.03",
        "decision": "non-harmful (MISS)"
      },
      {
        "name": "Mechanism A",
        "prob": "--",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism B",
        "prob": "0.98",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism D",
        "prob": "0.93",
        "decision": "HARMFUL"
      }
    ],
    "story": "The motivating case of the thesis: superficially non-hostile or joking wording carrying real hostility, recovered when the sarcasm signal is available."
  },
  {
    "title": "The honest trade-off: a false positive the mechanisms introduce",
    "language": "English",
    "text": "@user “ lets go gays ”",
    "true_label": "non-harmful",
    "sarcasm_score": "0.48",
    "source": "hatexplain",
    "models": [
      {
        "name": "Baseline",
        "prob": "0.43",
        "decision": "non-harmful"
      },
      {
        "name": "Mechanism A",
        "prob": "--",
        "decision": "HARMFUL (false positive)"
      },
      {
        "name": "Mechanism B",
        "prob": "0.77",
        "decision": "HARMFUL (false positive)"
      },
      {
        "name": "Mechanism D",
        "prob": "0.51",
        "decision": "HARMFUL (false positive)"
      }
    ],
    "story": "Every mechanism trades precision for recall. Showing a failure case demonstrates the evaluation was honest: the operating point shifts toward recall; the mechanisms do not dominate the baseline on both axes."
  },
  {
    "title": "Mechanism D's cross-lingual cost: Arabic harmful D misses",
    "language": "Arabic",
    "text": "صحيح انك انسان وضيع",
    "true_label": "harmful",
    "sarcasm_score": "0.83",
    "source": "lhsab",
    "models": [
      {
        "name": "Baseline",
        "prob": "0.99",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism A",
        "prob": "--",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism B",
        "prob": "0.98",
        "decision": "HARMFUL"
      },
      {
        "name": "Mechanism D",
        "prob": "0.05",
        "decision": "non-harmful (MISS)"
      }
    ],
    "story": "Mechanism D helps English but degrades Arabic (recall 0.8155 -> 0.7729, p=0.0009) -- a cost the constant-score ablation showed is independent of score values, and which generalized to French in zero-shot transfer (recall collapse to 0.15-0.18)."
  },
  {
    "title": "Mechanism D2: what reasoning injection looks like (pilot)",
    "language": "Arabic",
    "text": "صحيح انك انسان وضيع",
    "true_label": "harmful",
    "sarcasm_score": "0.83",
    "source": "lhsab",
    "models": [
      {
        "name": "Matched baseline",
        "prob": "--",
        "decision": "non-harmful (MISS)"
      },
      {
        "name": "Matched D (score)",
        "prob": "--",
        "decision": "HARMFUL"
      },
      {
        "name": "D2 (reasoning)",
        "prob": "--",
        "decision": "HARMFUL"
      }
    ],
    "story": "Generated reasoning for this example: “Literal meaning: It is true that you are a worthless person. Implied meaning: The speaker is insulting the addressee's worthiness and dignity. Speaker intention: To demean and criticize the addressee's value and status. Indirect language: No -- the statement is made directly without any implied irony or sarcasm.” — D2 significantly beats its matched baseline (p=0.0019, replicated with a second generator, p=0.0038) but does not exceed bare-score injection: the channel, not the content, is the binding constraint."
  }
]