{"id":"W2788539628","doi":"","title":"Legal Research, Legal Reasoning and Precedent in Canada in the Digital Age","year":2018,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Legal research; Law; Political science; Legal profession; Legal realism; Empirical legal studies; Context (archaeology); Scholarship; Legal opinion; Legal history; Common law; Comparative law; Black letter law; Private law; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006920822,0.00008092236,0.0001052788,0.000106668,0.0007279842,0.000518455,0.0005241761,0.00004898092,0.00002970449],"category_scores_gemma":[0.0009173567,0.0000633608,0.00002293336,0.0005171644,0.000561812,0.0005033502,0.00005372356,0.002030719,0.00001035873],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003685474,"about_ca_system_score_gemma":0.01251969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9584969,"about_ca_topic_score_gemma":0.9997472,"domain_scores_codex":[0.9959776,0.0005104779,0.0002546647,0.0001737892,0.0007886994,0.002294789],"domain_scores_gemma":[0.9993879,0.0002718179,0.00005568301,0.000108832,0.00008901268,0.00008674592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0000441382,0.00003175028,0.02057928,9.798865e-7,0.000009326865,0.00009644491,0.01099238,0.00001055705,0.00001874215,0.9372302,0.000358657,0.03062758],"study_design_scores_gemma":[0.0003022646,0.0005433591,0.008812093,0.0001234587,0.00000688681,0.0003306416,0.3949696,0.0002070316,0.0001422266,0.2973474,0.2968203,0.0003947943],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9310005,0.0003686468,0.00004507006,0.00277919,0.0001868233,0.0001611609,8.783061e-7,0.000005516074,0.06545226],"genre_scores_gemma":[0.997899,0.0004406487,0.000008027509,0.00007953011,0.0005635318,0.000004879713,3.616045e-7,0.00000795378,0.0009960968],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6398828,"threshold_uncertainty_score":0.9930784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03800270983484599,"score_gpt":0.3599387192816101,"score_spread":0.3219360094467641,"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."}}