{"id":"W4410256258","doi":"10.12731/2576-9634-2025-9-1-215","title":"THE PROBLEMS OF INTEGRATING ARTIFICIAL INTELLIGENCE INTO THE JUDICIAL SYSTEM OF RUSSIAN FEDERATION","year":2025,"lang":"en","type":"article","venue":"Russian Studies in Law and Politics","topic":"Digital Transformation in Law","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Russian federation; Political science; Artificial intelligence; Computer science; Law; Sociology; Regional science","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":[0.03365827,0.0002166388,0.0005295973,0.002125306,0.009307387,0.01141175,0.001842425,0.00349371,0.002629022],"category_scores_gemma":[0.03706022,0.0004243778,0.0008670065,0.002078435,0.01127968,0.005031139,0.005200211,0.003097904,0.0005883682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008560941,"about_ca_system_score_gemma":0.01414007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01533015,"about_ca_topic_score_gemma":0.008808749,"domain_scores_codex":[0.9660646,0.01987931,0.002952834,0.002809878,0.005460065,0.002833474],"domain_scores_gemma":[0.9781577,0.01141693,0.002648457,0.003383347,0.003748008,0.000645571],"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":[0.00002677128,0.00003038111,0.00754077,0.0001081782,0.0000320649,0.0004494459,0.01081493,0.002416567,0.0003480461,0.9352477,0.003301415,0.03968367],"study_design_scores_gemma":[0.00005701663,0.000198948,0.03868032,0.001353794,0.000171993,0.001291753,0.01992542,0.01394732,0.001915765,0.6239472,0.2983505,0.000160056],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4101067,0.008974602,0.07855979,0.07044175,0.001022344,0.0003510306,0.0002129972,0.0003052386,0.4300255],"genre_scores_gemma":[0.9832633,0.0007830705,0.009436715,0.001753154,0.0001714313,0.0001021021,0.00005126178,0.00002573919,0.004413196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03365827,"threshold_uncertainty_score":0.178004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04594394951925752,"score_gpt":0.2900333133948862,"score_spread":0.2440893638756287,"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."}}