{"id":"W2915908076","doi":"10.1177/1365712718813795","title":"Taking the dialectical stance in reasoning with evidence and proof","year":2018,"lang":"en","type":"article","venue":"The International Journal of Evidence & Proof","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Argumentation theory; Dialectic; Probabilistic logic; Probabilistic argumentation; Computer science; Epistemology; Defeasible reasoning; Process (computing); Order (exchange); Deductive reasoning; Artificial intelligence; Psychology; Cognitive science; Philosophy; Programming language","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":[0.02131459,0.001243367,0.001358725,0.006587113,0.003755032,0.01110975,0.00379836,0.005622028,0.005437779],"category_scores_gemma":[0.04398066,0.001140371,0.002611636,0.003884571,0.02412361,0.03045224,0.01163338,0.005788429,0.000876294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003833956,"about_ca_system_score_gemma":0.002505625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001671557,"about_ca_topic_score_gemma":0.001612194,"domain_scores_codex":[0.9776464,0.01464764,0.001390301,0.001852478,0.003985523,0.0004777441],"domain_scores_gemma":[0.9603273,0.02982533,0.002221218,0.004378799,0.002425484,0.0008217855],"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":[0.000007578069,0.00000610223,0.0001096711,0.00003903128,0.00001280269,0.00003821054,0.0004068919,0.0009850265,0.00004622313,0.994993,0.0001341204,0.003221417],"study_design_scores_gemma":[0.000007651112,0.00000764845,0.0000365844,0.00002897704,0.00001153882,0.00004027032,0.00009488437,0.00348915,0.0001079162,0.9932852,0.00288252,0.000007633736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006656783,0.001189107,0.9555424,0.008455078,0.0001927941,0.00008854717,0.00008047432,0.0001054881,0.02768933],"genre_scores_gemma":[0.4221971,0.001176514,0.5705655,0.001140637,0.0003290824,0.0004287116,0.0001207541,0.00009486991,0.003946895],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02131459,"threshold_uncertainty_score":0.1127237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2137807572191932,"score_gpt":0.4468774098081574,"score_spread":0.2330966525889642,"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."}}