{"id":"W7007984163","doi":"","title":"Artificial Intelligence & Criminal Justice: Cases and Commentary","year":2025,"lang":"en","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; Active listening","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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008404552,0.0001958587,0.0002341753,0.00009393689,0.001716685,0.0003289612,0.0004582395,0.0001601168,0.001061763],"category_scores_gemma":[0.001145676,0.0002102921,0.00007593761,0.0004311463,0.001533524,0.0004496732,0.0001755108,0.0002765893,0.000316881],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001712282,"about_ca_system_score_gemma":0.0001809098,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02441708,"about_ca_topic_score_gemma":0.04527345,"domain_scores_codex":[0.9978852,0.0002712936,0.0004703922,0.0004378378,0.0003845337,0.000550797],"domain_scores_gemma":[0.9981318,0.001039271,0.00008616862,0.0003142917,0.0001479192,0.0002805906],"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":[0.00005414266,0.00009645553,0.0008659405,0.0000471263,0.00002102015,0.00005081103,0.002716816,0.0000210667,0.0001832149,0.9583128,0.00597618,0.0316544],"study_design_scores_gemma":[0.00007535863,0.0002310696,0.0004176787,0.0003103625,0.0006707368,0.0000157484,0.1025937,0.0004301789,0.02480246,0.2820015,0.5874969,0.0009543135],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7609097,0.002502003,0.008874589,0.04023825,0.005867071,0.001380055,0.00006355023,0.0006410588,0.1795237],"genre_scores_gemma":[0.9881433,0.0002104771,0.00115935,0.008597067,0.0007049568,0.00003074883,0.000006038309,0.00001368009,0.001134444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6763114,"threshold_uncertainty_score":0.9998514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0640065502333781,"score_gpt":0.3706628001110951,"score_spread":0.3066562498777171,"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."}}