{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01016397,0.0007106108,0.001724008,0.003883704,0.003005809,0.0201329,0.001399123,0.0106151,0.08201719],"category_scores_gemma":[0.04231549,0.0008980625,0.0005630975,0.004564471,0.01551889,0.02776128,0.008399412,0.01143994,0.04170541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003328071,"about_ca_system_score_gemma":0.006679114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002285471,"about_ca_topic_score_gemma":0.00186847,"domain_scores_codex":[0.9902699,0.003558561,0.0008485122,0.001573787,0.003526517,0.0002227206],"domain_scores_gemma":[0.9598794,0.0247022,0.001508146,0.008503531,0.003605168,0.001801543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00001299459,0.0000172656,0.0002518449,0.0002000213,0.00001301343,0.0000426225,0.0002043386,0.00009336623,0.00008471397,0.7586706,0.18548,0.05492916],"study_design_scores_gemma":[0.000008923004,0.000006535273,0.00007588627,0.0001897563,0.000005825584,0.00008103035,0.0001393061,0.0003626648,0.00005911094,0.4684222,0.5306419,0.000006951848],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.000804361,0.03307753,0.03952362,0.4412757,0.01435616,0.00006656031,0.0009274101,0.0006612112,0.4693075],"genre_scores_gemma":[0.1154846,0.06670249,0.03430977,0.2044258,0.04777528,0.0006434858,0.001682186,0.001789866,0.5271866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08201719,"threshold_uncertainty_score":0.2743748,"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."}}