{"id":"W4323306796","doi":"10.32920/22223251","title":"Teaching Emerging Technologies as Legal Systems: Proposals for a Changing Law School Curriculum","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Artificial Intelligence in Law","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Curriculum; Legal education; Core curriculum; Engineering ethics; Legal research; Legal aspects of computing; Political science; Sociology; Engineering; Law; Pedagogy; Computer science; The Internet","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":[0.006649703,0.0002902137,0.0002817492,0.001589439,0.002861426,0.009906488,0.002062883,0.004518568,0.009104043],"category_scores_gemma":[0.006772076,0.0003491046,0.0004684294,0.001215958,0.009172508,0.0107804,0.004354594,0.006453512,0.001951157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006590933,"about_ca_system_score_gemma":0.01482174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002259185,"about_ca_topic_score_gemma":0.003901105,"domain_scores_codex":[0.9968998,0.001618658,0.0001483437,0.0003381242,0.0006617329,0.0003334026],"domain_scores_gemma":[0.9955001,0.001892337,0.0002952876,0.0003211889,0.0005993524,0.001391733],"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":[0.000009439695,0.0004928763,0.001105622,0.000149902,0.000002618612,0.00009369276,0.007697753,0.0008800525,0.0008139559,0.9082947,0.01078373,0.06967559],"study_design_scores_gemma":[0.0000505746,0.0001728806,0.002016359,0.0004242233,0.000009089449,0.0001577328,0.009545458,0.005673212,0.001365272,0.5485552,0.4320056,0.00002437116],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.09733761,0.00383832,0.22996,0.3240629,0.002167802,0.0008540645,0.0001088894,0.001062897,0.3406076],"genre_scores_gemma":[0.573311,0.00639994,0.2838868,0.02254547,0.001087976,0.001690354,0.0002349521,0.0002619918,0.1105814],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.009906488,"threshold_uncertainty_score":0.04782081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06849624014835407,"score_gpt":0.4038011653638455,"score_spread":0.3353049252154914,"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."}}