{"id":"W4285296603","doi":"10.2139/ssrn.4077216","title":"Data Science Meets Law","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"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"],"consensus_categories":[],"category_scores_codex":[0.01556569,0.00008557663,0.0001033571,0.0001077786,0.007558601,0.0002195063,0.003498247,0.00002619992,0.0007620844],"category_scores_gemma":[0.0003685404,0.00008864878,0.00004341545,0.0008743498,0.001374238,0.001292162,0.0007080902,0.001789303,0.00008588055],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002544892,"about_ca_system_score_gemma":0.01041024,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003934122,"about_ca_topic_score_gemma":0.04066223,"domain_scores_codex":[0.9948124,0.0002953402,0.0002449243,0.0003403735,0.001378863,0.002928112],"domain_scores_gemma":[0.9990321,0.00008638002,0.0001366522,0.0004676995,0.0001202886,0.0001568715],"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":[0.000008560063,0.00003593845,0.00006437688,2.165874e-7,0.000009309123,0.000003858257,0.001470415,0.00006394754,0.0002270705,0.9875057,0.0002368005,0.01037381],"study_design_scores_gemma":[0.00003852282,0.00008648456,0.000004722097,0.000001529376,0.000008833332,0.00007184979,0.02945996,0.0001213294,0.00008403982,0.3620898,0.6079161,0.0001168914],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2198465,0.007430025,0.006645339,0.06733558,0.008655662,0.001009469,0.0001041808,0.0004809022,0.6884923],"genre_scores_gemma":[0.9963984,0.0007396788,0.00009827207,0.0004319472,0.0005195779,0.000004914522,0.000003021649,0.00001193685,0.001792244],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7765519,"threshold_uncertainty_score":0.9951998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07241716785567114,"score_gpt":0.3881776368020666,"score_spread":0.3157604689463955,"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."}}