{"id":"W2265282365","doi":"","title":"Of Priors and of Disconnects: How 'Chicago' Premises Risk Distortion","year":2014,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Legal principles and applications","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Boilerplate text; Prior probability; Undoing; Proposition; Law and economics; Law; Epistemology; Political science; Computer science; Sociology; Artificial intelligence; Philosophy; Psychology; Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009210484,0.0004431824,0.0006807619,0.00140855,0.004001209,0.01039974,0.002375149,0.00634003,0.008986839],"category_scores_gemma":[0.03267944,0.0006052837,0.0006325783,0.0009010732,0.03565538,0.0184707,0.005743111,0.01204011,0.0008473643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006559972,"about_ca_system_score_gemma":0.003141653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006816765,"about_ca_topic_score_gemma":0.004371716,"domain_scores_codex":[0.9925849,0.003676885,0.0002032267,0.001143646,0.001799631,0.0005916515],"domain_scores_gemma":[0.9843112,0.01141207,0.0007229344,0.001866221,0.001299208,0.0003884561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00000367158,0.000001935835,0.00005609182,0.000003331264,0.00000118037,0.00001162454,0.0004991184,0.00008371273,0.000008504589,0.9974572,0.0009148926,0.0009587288],"study_design_scores_gemma":[0.000004021632,0.000001952882,0.00007406858,0.00001855809,0.000001734061,0.00001872477,0.0002573764,0.0003005436,0.00003121467,0.9910499,0.008237204,0.000004705856],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.05113785,0.004967019,0.1576055,0.1585087,0.0009843776,0.00007954065,0.0002546386,0.0001995645,0.6262629],"genre_scores_gemma":[0.9660628,0.001042107,0.009054333,0.007737303,0.0007305466,0.0001008514,0.00005157859,0.0001715074,0.01504901],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01039974,"threshold_uncertainty_score":0.04871029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01295997421133903,"score_gpt":0.2638875613829454,"score_spread":0.2509275871716063,"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."}}