{"id":"W3109956373","doi":"10.2139/ssrn.3734656","title":"AI and Administrative Law","year":2020,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Law, AI, and Intellectual Property","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Administrative law; Law; Political science; Business","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":[0.002512497,0.0003276527,0.0004603568,0.001352015,0.005057063,0.01089196,0.0007290995,0.00490303,0.02882499],"category_scores_gemma":[0.009631928,0.0003060916,0.000357919,0.00168199,0.01564792,0.006350105,0.001817142,0.006500717,0.004506461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004430224,"about_ca_system_score_gemma":0.003831755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009833268,"about_ca_topic_score_gemma":0.009912846,"domain_scores_codex":[0.9982485,0.0008881631,0.00005626978,0.0002556781,0.0004020839,0.0001493308],"domain_scores_gemma":[0.9957983,0.002770118,0.0002329435,0.0004547816,0.0004744871,0.0002694477],"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":[0.000003808033,0.00001040544,0.0001525594,0.00001060297,0.000002463007,0.00001149162,0.0003534243,0.00005897291,0.0000184466,0.9771348,0.01802466,0.004218436],"study_design_scores_gemma":[0.00000808308,0.000007921736,0.0003291821,0.00007810834,0.000004373434,0.00003569396,0.0005215189,0.0002867969,0.00004320147,0.8227377,0.1759425,0.000004850743],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.004895994,0.03236921,0.004751805,0.09878213,0.0009029269,0.00001679032,0.0001004469,0.00008430422,0.8580964],"genre_scores_gemma":[0.6633207,0.01757345,0.0036565,0.03008463,0.005474799,0.0001422737,0.0001317331,0.0001520302,0.279464],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02882499,"threshold_uncertainty_score":0.09642917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0270744047049677,"score_gpt":0.2541231654614977,"score_spread":0.22704876075653,"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."}}