{"id":"W4321125754","doi":"10.2139/ssrn.4360341","title":"Man vs. Machine: Technological Promise and Political Limits of Automated Regulation Enforcement","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Regulation and Compliance Studies","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Impact","funders":"","keywords":"Politics; Enforcement; Law and economics; Business; Computer security; Political science; Risk analysis (engineering); Computer science; Economics; Law","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":[0.01852207,0.0002258167,0.000698983,0.002310136,0.00471396,0.01397463,0.0015633,0.008838733,0.01277711],"category_scores_gemma":[0.05455447,0.0005101381,0.000498842,0.001923754,0.03456931,0.01876576,0.004069846,0.007750057,0.0008664989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002874587,"about_ca_system_score_gemma":0.003275936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002711307,"about_ca_topic_score_gemma":0.002062961,"domain_scores_codex":[0.9858753,0.007522292,0.0004293727,0.001696935,0.00323697,0.001239186],"domain_scores_gemma":[0.9033433,0.08112893,0.005726576,0.006107728,0.002653606,0.001039894],"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":[0.00003355453,0.00002595825,0.0008457939,0.00001718578,0.000007248404,0.00002864894,0.0007584196,0.0003012343,0.00008951246,0.9925795,0.000834921,0.004478072],"study_design_scores_gemma":[0.00002720074,0.00003567267,0.002293767,0.00005985107,0.0000135965,0.00006568841,0.001336596,0.001646339,0.0002137782,0.9847628,0.009524988,0.00001958409],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1922032,0.005824273,0.030277,0.1825642,0.0003941188,0.00005039436,0.0001619583,0.00008170658,0.5884432],"genre_scores_gemma":[0.9942544,0.0004420558,0.0006639847,0.002299818,0.0002928159,0.00003271952,0.00001187839,0.00001968119,0.001982532],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01852207,"threshold_uncertainty_score":0.09795523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01643494839139756,"score_gpt":0.2564977147850577,"score_spread":0.2400627663936601,"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."}}