{"id":"W2887615026","doi":"10.3386/w24888","title":"Priors rule: When do Malfeasance Revelations Help or Hurt Incumbent Parties?","year":2018,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Corruption and Economic Development","field":"Social Sciences","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University","keywords":"Prior probability; Business; Law and economics; Economics; Computer science; Bayesian probability; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002669151,0.0004764811,0.0007316539,0.0006160027,0.0008183378,0.002114573,0.0007943211,0.001696654,0.0185886],"category_scores_gemma":[0.02552513,0.0006262246,0.0005809306,0.0002944357,0.001677802,0.003035306,0.0009106962,0.002782097,0.002880628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009228368,"about_ca_system_score_gemma":0.0007639115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003183039,"about_ca_topic_score_gemma":0.004990669,"domain_scores_codex":[0.9987084,0.0004039526,0.00006424089,0.0003537316,0.0002519243,0.000217781],"domain_scores_gemma":[0.9912961,0.003776213,0.002458164,0.001183321,0.00059283,0.0006932888],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004799686,0.002003051,0.278162,0.0005850578,0.0005696286,0.001161551,0.00317838,0.02344431,0.02096711,0.3140439,0.021846,0.3292393],"study_design_scores_gemma":[0.0006579796,0.001244187,0.3401867,0.0003117956,0.0004955161,0.001100031,0.002521113,0.0759014,0.01305714,0.5355741,0.0287166,0.0002332866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7918187,0.0008388296,0.06236769,0.01110028,0.0002377673,0.0003678548,0.001160269,0.0003542138,0.1317543],"genre_scores_gemma":[0.9878603,0.0002179927,0.003346727,0.0005171876,0.00007541959,0.00004382215,0.0002242322,0.00003539092,0.007679026],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0185886,"threshold_uncertainty_score":0.06218505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5417565005604961,"score_gpt":0.5711762927372351,"score_spread":0.02941979217673907,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}