{"id":"W3123285973","doi":"","title":"Regulatory Reform in Ontario: Machine Learning and Regulation","year":2018,"lang":"en","type":"article","venue":"C.D. Howe Institute Commentary","topic":"Legal Education and Practice Innovations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Big data; Analytics; Government (linguistics); Computer science; Business; Artificial intelligence; Data science; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007159617,0.00007766683,0.00008508398,0.0001403516,0.0008291079,0.0001011351,0.0001021324,0.0000657212,0.00086419],"category_scores_gemma":[0.0001071001,0.00008161223,0.00001520753,0.0003179229,0.0003925004,0.00137488,0.00004732928,0.0003986141,0.00002834003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008754715,"about_ca_system_score_gemma":0.0004249902,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5289292,"about_ca_topic_score_gemma":0.8946,"domain_scores_codex":[0.9991853,0.0001024112,0.0001945022,0.0001600522,0.0001758854,0.000181848],"domain_scores_gemma":[0.9995909,0.00004334089,0.00009162506,0.0001190107,0.00008136639,0.00007375073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001214532,0.0003022842,0.6046584,0.00001547326,0.00004427239,0.000009510532,0.07574199,0.000009399395,0.0003159426,0.2463999,0.0320122,0.04036923],"study_design_scores_gemma":[0.0002452217,0.00002975396,0.08821424,0.00002111235,0.000005517282,0.000001923221,0.002164608,0.00002944033,0.0000410182,0.0009929369,0.9081576,0.00009665612],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6975901,0.00009029306,0.00005273029,0.09683147,0.001405244,0.0002288675,0.000001935101,0.00007222535,0.2037271],"genre_scores_gemma":[0.9881265,0.00004538219,0.0009476228,0.004938902,0.0002911453,0.00001066655,0.00006535573,0.000006409885,0.005568039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8761454,"threshold_uncertainty_score":0.9462274,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04114407067987638,"score_gpt":0.3422707816455392,"score_spread":0.3011267109656628,"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."}}