{"id":"W7103506468","doi":"","title":"Artificial Intelligence & Criminal Justice: Cases and Commentary","year":2025,"lang":"","type":"article","venue":"eYLS (Yale Law School)","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Casebook; Criminal justice; Enthusiasm; Reading (process); Prison; Economic Justice; Criminal law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["sts","insufficient_payload"],"category_scores_codex":[0.002064083,0.0007324033,0.0008061545,0.0002572687,0.004366949,0.001225808,0.001301793,0.0006053492,0.00554364],"category_scores_gemma":[0.002686836,0.0008315193,0.0002897728,0.001395775,0.005437699,0.00112538,0.0007573827,0.001281171,0.001865334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005315086,"about_ca_system_score_gemma":0.0005853776,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03965889,"about_ca_topic_score_gemma":0.09711701,"domain_scores_codex":[0.993342,0.0009517907,0.001706199,0.001416184,0.001019948,0.001563891],"domain_scores_gemma":[0.994319,0.003006179,0.0003825998,0.0009719742,0.0004461089,0.0008741849],"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":[0.0003060316,0.0005420962,0.0009712015,0.0004006755,0.0001416345,0.000127061,0.008957545,0.000102942,0.0003720765,0.9111366,0.01614421,0.06079793],"study_design_scores_gemma":[0.0001239457,0.0004577824,0.0002107733,0.001115839,0.002683319,0.00004157331,0.1024264,0.00079993,0.01379095,0.2082448,0.6681411,0.00196357],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7463461,0.01434087,0.01761702,0.06343444,0.02303153,0.003677325,0.000387679,0.0007137079,0.1304513],"genre_scores_gemma":[0.9760412,0.001776151,0.001906959,0.01461815,0.002093208,0.00006710157,0.00002480817,0.00004928375,0.003423151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7028918,"threshold_uncertainty_score":0.999811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0765619148943795,"score_gpt":0.3730312449719216,"score_spread":0.2964693300775421,"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."}}