{"id":"W2266597856","doi":"","title":"Justice is blind when it comes to Canadian jury selection","year":2015,"lang":"en","type":"article","venue":"","topic":"Jury Decision Making Processes","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Jury selection; Economic Justice; Jury; Selection (genetic algorithm); Political science; Law; Criminology; Computer science; Psychology; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.06917459,0.0006824743,0.001609286,0.004977818,0.03714291,0.02235529,0.003564307,0.008836899,0.01187631],"category_scores_gemma":[0.3149185,0.001132154,0.00109073,0.004276194,0.02064567,0.007125446,0.008925638,0.0110254,0.0009569032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.09047605,"about_ca_system_score_gemma":0.2514994,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9406621,"about_ca_topic_score_gemma":0.9555054,"domain_scores_codex":[0.9204756,0.02961848,0.002429696,0.005196061,0.02533268,0.01694744],"domain_scores_gemma":[0.8179349,0.1161782,0.008200237,0.006125716,0.03657889,0.01498213],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008194249,0.0003263159,0.06803931,0.0003155637,0.0003070753,0.001140985,0.1152055,0.004090772,0.0007571904,0.4974899,0.1547172,0.1567909],"study_design_scores_gemma":[0.0001991158,0.0001842365,0.130043,0.000962077,0.0003078341,0.00054316,0.09277915,0.00936186,0.001503679,0.5048155,0.2583778,0.0009226906],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3068967,0.004614493,0.01807647,0.2475772,0.002858076,0.0005991586,0.000546219,0.0001912243,0.4186404],"genre_scores_gemma":[0.9654132,0.0009272813,0.003571282,0.009701972,0.0003229138,0.00009507883,0.00009768861,0.00008005677,0.01979053],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.09047605,"threshold_uncertainty_score":0.6564528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2091613485453275,"score_gpt":0.4359453966142839,"score_spread":0.2267840480689564,"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."}}