{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002860843,0.001173662,0.001222979,0.002407731,0.0007450308,0.001622854,0.002448912,0.002277464,0.003630896],"category_scores_gemma":[0.01309779,0.0003881318,0.0009466868,0.001499786,0.001350213,0.002875344,0.001441923,0.004932943,0.001555424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00236275,"about_ca_system_score_gemma":0.001535814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006818128,"about_ca_topic_score_gemma":0.0103425,"domain_scores_codex":[0.9982061,0.0005125211,0.0001153883,0.0006270485,0.0003797258,0.000159174],"domain_scores_gemma":[0.9943261,0.003678775,0.000470184,0.0005297619,0.0007458568,0.000249334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009656748,0.001460925,0.02286743,0.0004190048,0.0002689781,0.0006562998,0.0003398519,0.3522768,0.004726958,0.02098662,0.04958563,0.5454459],"study_design_scores_gemma":[0.00002811217,0.00004597269,0.001190065,0.00002239923,0.00001796528,0.00004402906,0.00002101388,0.9824932,0.001426408,0.01276988,0.001926719,0.00001429051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2523223,0.007286905,0.703815,0.005371475,0.0008055671,0.0005643219,0.0045344,0.006983018,0.01831709],"genre_scores_gemma":[0.9134531,0.0005211575,0.07515404,0.0009543038,0.0003560135,0.0001671561,0.004606485,0.0001235262,0.004664246],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006818128,"threshold_uncertainty_score":0.01714295,"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."}}