{"id":"W2895460886","doi":"10.1017/s1352325218000113","title":"REASONING BY PRECEDENT—BETWEEN RULES AND ANALOGIES","year":2018,"lang":"en","type":"article","venue":"Legal Theory","topic":"Judicial and Constitutional Studies","field":"Social Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Analogy; Analogical reasoning; Discretion; Argument (complex analysis); Defeasible reasoning; Process (computing); Deductive reasoning; Epistemology; Case-based reasoning; Computer science; Law; Artificial intelligence; Political science; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0004482082,0.00005496202,0.00008634486,0.00001687268,0.0009165187,0.00006585995,0.00009780302,0.00003974464,0.0000861062],"category_scores_gemma":[0.000283561,0.0000468098,0.00002006165,0.0000732047,0.001574481,0.0001520175,0.00005850548,0.00005280545,0.00006049179],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002109843,"about_ca_system_score_gemma":0.00004110573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002849763,"about_ca_topic_score_gemma":0.0006418627,"domain_scores_codex":[0.9993839,0.0001164152,0.00006796696,0.0001184291,0.0001420825,0.0001712061],"domain_scores_gemma":[0.9996798,0.0001597178,0.00002678251,0.00004346767,0.00004307967,0.00004714048],"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":[0.000008411902,0.0000042844,0.007853884,7.488753e-7,0.00002067832,6.324191e-7,0.001677514,1.633196e-8,0.00003255792,0.9738007,0.001762961,0.0148376],"study_design_scores_gemma":[0.00009249862,0.00004317142,0.02230109,0.00003269755,0.00002274777,5.277435e-7,0.004484201,4.781257e-7,0.0002190729,0.5404159,0.4322484,0.0001391306],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2981688,0.001791642,0.0004012479,0.0009878195,0.0001767952,0.00006716604,0.00001696297,0.0000834352,0.6983061],"genre_scores_gemma":[0.9970961,0.0001775853,0.00009810057,0.0001510388,0.0006821244,0.000003899382,0.000002067648,0.000002511569,0.001786534],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6989273,"threshold_uncertainty_score":0.7049213,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967771035387806,"score_gpt":0.3037214548208461,"score_spread":0.2840437444669681,"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."}}