{"id":"W4317209756","doi":"10.1145/3580489","title":"Contrastive Learning for Legal Judgment Prediction","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Task (project management); Representation (politics); Artificial intelligence; Range (aeronautics); Focus (optics); Machine learning; Law; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009975084,0.000090637,0.0001173563,0.0002369856,0.001357735,0.0002520609,0.0001848606,0.0001167306,0.00006002039],"category_scores_gemma":[0.0003276069,0.00009465974,0.00008168307,0.0005041431,0.00009760077,0.001631971,0.000002483743,0.0001531731,0.0009533041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002194803,"about_ca_system_score_gemma":0.0001078082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079074,"about_ca_topic_score_gemma":0.000146667,"domain_scores_codex":[0.9986377,0.0001048798,0.0004406546,0.000104345,0.0004301798,0.0002822215],"domain_scores_gemma":[0.998884,0.0004580537,0.0001411907,0.0001508923,0.0002808996,0.0000849627],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002151164,0.00007549888,0.0002730465,0.0001201877,0.0001462499,9.081631e-7,0.07244509,0.6366417,0.0001154395,0.1369949,0.007094375,0.1458775],"study_design_scores_gemma":[0.0002279819,0.0001695394,0.0001404029,0.00006475602,0.00002311033,0.00000115575,0.07336137,0.03261114,0.0006020645,0.0004114108,0.8922248,0.0001623147],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006972698,0.000006625729,0.96614,0.001380723,0.003762672,0.001555446,0.0001071742,0.0009215079,0.01915311],"genre_scores_gemma":[0.9963579,0.0000382045,0.0001110737,0.00007097768,0.00019077,0.0005518282,0.00005117059,0.000008137773,0.002619975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9893852,"threshold_uncertainty_score":0.9999424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05367930586555467,"score_gpt":0.3340259175124735,"score_spread":0.2803466116469188,"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."}}