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            "instruction": {
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                -0.003
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              "wilcoxon_z": -2.0,
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            "plan": {
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              "median_delta_h_minus_ai": -0.028,
              "median_delta_95ci": [
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              "wilcoxon_z": -0.79,
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            "self_refine": {
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              "median_delta_h_minus_ai": 0.028,
              "median_delta_95ci": [
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          },
          "gini": {
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            },
            "fewshot": {
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              "median_delta_h_minus_ai": 0.06,
              "median_delta_95ci": [
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            "instruction": {
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            "self_refine": {
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          },
          "seg_entropy": {
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              "median_delta_h_minus_ai": -0.001,
              "median_delta_95ci": [
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                0.062
              ],
              "wilcoxon_z": 1.18,
              "wilcoxon_p": 0.23659,
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            },
            "fewshot": {
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              "paired_dz": 0.441
            },
            "instruction": {
              "n_pairs": 200,
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              "median_delta_95ci": [
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                0.045
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              "wilcoxon_p": 0.59051,
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              "paired_dz": -0.037
            },
            "plan": {
              "n_pairs": 200,
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              "median_delta_95ci": [
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                0.102
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            },
            "self_refine": {
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              "median_delta_h_minus_ai": 0.029,
              "median_delta_95ci": [
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                0.082
              ],
              "wilcoxon_z": 1.22,
              "wilcoxon_p": 0.2224,
              "matched_rank_biserial": 0.1,
              "paired_dz": 0.087
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          }
        },
        "consistency_B_n_rank_biserial": {
          "control": {
            "it0": 0.608,
            "it1": 0.637,
            "it2": 0.613
          },
          "fewshot": {
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            "it1": 0.724,
            "it2": 0.725
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          "instruction": {
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            "it1": 0.374,
            "it2": 0.3
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          "plan": {
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          },
          "self_refine": {
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            "it1": 0.176,
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        },
        "note": "Per headline: metric vector averaged across available seeds, then ONE paired Wilcoxon on independent headline pairs (pooling seeds as pairs would pseudo-replicate the human docs). delta = human - model."
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        "n_tests": 20,
        "n_raw_sig": 13,
        "n_holm": 12,
        "n_bh": 12,
        "lost_under_bh": [
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        "lost_under_holm": [
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        "tests": [
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    }
  ]
}
