{"id":"W3082206496","doi":"10.5539/jpl.v13n3p286","title":"Can Artificial Intelligence Author Laws: A Perspective from Russia","year":2020,"lang":"en","type":"article","venue":"Journal of Politics and Law","topic":"Digital Transformation in Law","field":"Economics, Econometrics and Finance","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Foundation for Basic Research","keywords":"Context (archaeology); Legislature; Automation; Obstacle; Digital economy; Process (computing); Quality (philosophy); Digital transformation; Field (mathematics); Computer science; Political science; Law; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.004214261,0.0004391399,0.0007885123,0.002638602,0.00734575,0.01138969,0.00126768,0.008025926,0.002884772],"category_scores_gemma":[0.004521915,0.0003713683,0.0008602836,0.001721888,0.02162628,0.009740072,0.004319566,0.006105364,0.0007569841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01316456,"about_ca_system_score_gemma":0.00453445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01157724,"about_ca_topic_score_gemma":0.006516523,"domain_scores_codex":[0.9948636,0.003314907,0.0001492077,0.0003642552,0.000688135,0.0006200174],"domain_scores_gemma":[0.9969776,0.002195428,0.0003005344,0.0001403589,0.0002572753,0.0001287909],"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":[0.000005109774,0.000005759502,0.0001583877,0.00002516067,0.000003426936,0.00008286697,0.002843506,0.0002128241,0.00003167472,0.9942654,0.0007970801,0.001568833],"study_design_scores_gemma":[0.00001202428,0.00003537229,0.001174349,0.0003723184,0.00001598445,0.0002144516,0.006853275,0.0009437594,0.0002006026,0.7887753,0.2013785,0.00002401369],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03980414,0.03819013,0.008931156,0.1533322,0.0009780251,0.0000323139,0.00009859907,0.00003726034,0.7585962],"genre_scores_gemma":[0.9471224,0.02109938,0.001891073,0.01073501,0.001101415,0.00006619601,0.00004584852,0.00004069631,0.0178981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01316456,"threshold_uncertainty_score":0.09551603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06585819335276263,"score_gpt":0.2660978248095473,"score_spread":0.2002396314567847,"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."}}