{"id":"W4213125073","doi":"10.33693/2223-0092-2021-11-4-107-121","title":"Analysis of Intrabranch and Legal Regulation of Artificial Intelligence Technologies Using the Example of International Experience, the Experience of Foreign Countries and the Russian Federation","year":2021,"lang":"en","type":"article","venue":"Sociopolitical sciences","topic":"Legal and Policy Issues","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"China; Russian federation; Political science; Politics; European union; Law; Business; International trade; Economic policy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01100097,0.0001848316,0.0002521598,0.002205921,0.006323071,0.006716385,0.0005091099,0.001017604,0.001452232],"category_scores_gemma":[0.009737447,0.0002204476,0.0002075834,0.003084378,0.008577339,0.003919417,0.002828147,0.001779735,0.000138822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005489332,"about_ca_system_score_gemma":0.006148264,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0129552,"about_ca_topic_score_gemma":0.01262473,"domain_scores_codex":[0.9883049,0.00770104,0.0004826657,0.0005976806,0.001532357,0.00138144],"domain_scores_gemma":[0.9916477,0.00550492,0.001219908,0.0004342079,0.0008211189,0.0003720104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003665139,0.00009662629,0.02890579,0.0001535265,0.00002097773,0.001801705,0.685816,0.0005367063,0.0008834688,0.2514367,0.001977121,0.02833467],"study_design_scores_gemma":[0.000008182885,0.0001002323,0.05132994,0.0004984376,0.00002601321,0.0007847853,0.7737614,0.0007089509,0.001118458,0.01053137,0.1610802,0.00005205256],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8727592,0.001502898,0.001297225,0.002589162,0.00003521319,0.00002330835,0.00002419025,0.000008654069,0.1217603],"genre_scores_gemma":[0.9972084,0.0004611994,0.0001637629,0.0001399797,0.000007491296,0.00000793327,0.00001031836,0.000004904711,0.001996036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0129552,"threshold_uncertainty_score":0.05817932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06577655356218645,"score_gpt":0.3821141627568833,"score_spread":0.3163376091946969,"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."}}