{"id":"W2972050110","doi":"","title":"Turning Standards into Rules Part 4: Machine Learning and Economic Substance","year":2018,"lang":"en","type":"article","venue":"TSpace","topic":"Legal and Constitutional Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Database transaction; Intuition; Revenue; Directive; Statute; Substance over form; Transaction cost; Harmonization; Public economics; Law and economics; Business; Actuarial science; Economics; Law; Computer science; Political science; Accounting; Finance; Psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0003188591,0.0001081546,0.0002128572,0.00005379275,0.0003451954,0.00006297323,0.00007056021,0.00003730056,0.0006751795],"category_scores_gemma":[0.00006584994,0.0001137337,0.00003433702,0.00004559035,0.0002388619,0.0001264305,0.00005443183,0.0001141255,0.0005313734],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000127526,"about_ca_system_score_gemma":0.00002255294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001104483,"about_ca_topic_score_gemma":0.0008050144,"domain_scores_codex":[0.9993203,0.000007930326,0.0001981459,0.000264837,0.00002776745,0.0001809984],"domain_scores_gemma":[0.9996889,0.00003236307,0.0001161318,0.00008156036,0.00002900078,0.00005210554],"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":[0.00006084948,0.00001964127,0.2341392,0.00002653931,0.0001118329,0.000004867638,0.005641828,0.00004322876,0.00005417467,0.7534033,0.003297528,0.003196995],"study_design_scores_gemma":[0.0003336729,0.0000705096,0.009234844,0.00001830238,0.000004012187,0.000004415013,0.0001982153,0.0007625348,0.0001348045,0.01531996,0.9737013,0.0002174166],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8528875,0.01833448,0.001249362,0.001495354,0.000440725,0.00007163845,0.0000912871,0.00006699384,0.1253627],"genre_scores_gemma":[0.9938291,0.001101006,0.000513037,0.00009558605,0.0002915934,0.000006547607,0.000006759079,0.00001003245,0.004146374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9704038,"threshold_uncertainty_score":0.7392742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02193964990656957,"score_gpt":0.2657409868393762,"score_spread":0.2438013369328067,"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."}}