{"id":"W2773616646","doi":"10.22329/wyaj.v34i1.4999","title":"BUILDING BETTER LAW: HOW DESIGN THINKING CAN HELP US BE BETTER LAWYERS, MEET NEW CHALLENGES, AND CREATE THE FUTURE OF LAW","year":2017,"lang":"en","type":"article","venue":"Windsor Yearbook of Access to Justice","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"York University; J.W. McConnell Family Foundation","keywords":"Mindset; Variety (cybernetics); Design thinking; Relevance (law); Law; Context (archaeology); Set (abstract data type); Engineering ethics; Practice of law; Legal profession; Order (exchange); Sociology; Political science; Computer science; Law and economics; Business; Engineering; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005740243,0.0002553165,0.0003727484,0.00006521319,0.000537914,0.0008696064,0.003197631,0.0001372605,0.00002147811],"category_scores_gemma":[0.00009349261,0.0001725108,0.00008005618,0.00006806991,0.0003636456,0.001337468,0.001195032,0.0002263392,0.000003266226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002656681,"about_ca_system_score_gemma":0.00007345039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001342629,"about_ca_topic_score_gemma":0.0005146675,"domain_scores_codex":[0.9982038,0.0001388322,0.0002640475,0.0005084363,0.0004847308,0.0004002051],"domain_scores_gemma":[0.9979073,0.0002508434,0.0002808916,0.001233003,0.0001556305,0.0001723758],"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":[0.0002879986,0.0001818164,0.0003400152,0.001025289,0.0005402975,0.00008028418,0.03698802,0.0003021678,0.01476866,0.7792351,0.06345858,0.1027917],"study_design_scores_gemma":[0.004195933,0.002112665,0.01198834,0.002316041,0.001456404,0.0001179229,0.001193189,0.01101251,0.239831,0.08950786,0.6330979,0.003170187],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.109561,0.01615303,0.170998,0.5521435,0.004694949,0.003593773,0.00005853226,0.0004652638,0.142332],"genre_scores_gemma":[0.9474621,0.0004794875,0.03561903,0.01455354,0.001374669,0.0000102053,7.802909e-7,0.00004444423,0.0004556741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8379012,"threshold_uncertainty_score":0.8385635,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08205352888496954,"score_gpt":0.2842031446469419,"score_spread":0.2021496157619724,"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."}}