{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.004386434,0.0003956267,0.0005648494,0.0004838328,0.002720391,0.001189689,0.001466172,0.0008094389,0.00006864815],"category_scores_gemma":[0.003805102,0.0003697766,0.0002991607,0.0004346182,0.0006109859,0.0004753031,0.001620419,0.001238523,0.000552621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005387393,"about_ca_system_score_gemma":0.0005824025,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1520933,"about_ca_topic_score_gemma":0.0135706,"domain_scores_codex":[0.9959338,0.0002859954,0.0007266436,0.0009347508,0.0007683843,0.001350429],"domain_scores_gemma":[0.9981752,0.0003562264,0.000375281,0.000615223,0.0003382237,0.0001398427],"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":[0.000003367617,0.00003249217,0.0001006718,0.0002472512,0.00005993781,0.00001223115,0.004679178,0.0004566496,0.00005561451,0.989801,0.001786792,0.002764748],"study_design_scores_gemma":[0.00005307915,0.00006120828,3.905254e-7,0.001548894,0.00008638939,0.00000319058,0.513639,0.009854364,0.001583398,0.2107674,0.2613559,0.001046819],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.04160455,0.00205251,0.2045817,0.06163882,0.03370501,0.02332347,0.0001671427,0.03839437,0.5945324],"genre_scores_gemma":[0.9569475,0.00009439106,0.005486094,0.0001462391,0.001848582,0.002090322,0.00002191028,0.0001116721,0.03325329],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9153429,"threshold_uncertainty_score":0.9998754,"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."}}