{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004437667,0.0008864681,0.0007329488,0.004528253,0.01692043,0.008735238,0.004058056,0.02115086,0.01129848],"category_scores_gemma":[0.02350214,0.0005869169,0.0008659773,0.005341708,0.01397781,0.005867112,0.004386531,0.01509842,0.003244623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02231316,"about_ca_system_score_gemma":0.01037068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08444571,"about_ca_topic_score_gemma":0.1163688,"domain_scores_codex":[0.9941015,0.0023128,0.0002995623,0.0005925603,0.001903545,0.0007900472],"domain_scores_gemma":[0.9879069,0.009332198,0.0005007549,0.0002952074,0.001433309,0.0005315655],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000009461788,0.00001320856,0.00006335958,0.0001714055,0.000003210859,0.0007291547,0.005559833,0.00006734886,0.00005310927,0.05989461,0.9277514,0.005683956],"study_design_scores_gemma":[0.000003605161,0.000003548751,0.0003316429,0.000721966,0.000002984904,0.0002428333,0.006893182,0.00009149827,0.00007452716,0.009150011,0.9824668,0.00001733181],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003864624,0.03891557,0.0005630577,0.7611676,0.03763955,0.0001153419,0.0002930585,0.00007204142,0.1573692],"genre_scores_gemma":[0.1452761,0.06664985,0.001308224,0.5503982,0.0432294,0.0006670977,0.0003699177,0.0002407863,0.1918604],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08444571,"threshold_uncertainty_score":0.1679084,"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."}}