{"id":"W3202155298","doi":"","title":"СВОЕВРЕМЕННЫЕ И АДЕКВАТНЫЕ МЕРЫ ПО ПОДДЕРЖКЕ ЭКОНОМИКИ И НАСЕЛЕНИЯ РОССИИ В ПЕРИОД ПАНДЕМИИ","year":2021,"lang":"ru","type":"article","venue":"Вестник РГГУ. Серия «Экономика. Управление. Право»","topic":"Regional Socio-Economic Development Trends","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Population; Government (linguistics); Economic recovery; Business; Quarter (Canadian coin); Economy; Russian economy; Economic sector; Christian ministry; Depreciation (economics); Economic policy; Economic growth; Political science; Economics; Geography; Economic system; Human capital; Capital formation","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science","research_integrity","insufficient_payload"],"consensus_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"category_scores_codex":[0.005440896,0.003792047,0.004639661,0.0013406,0.005387155,0.002833048,0.005583536,0.003516811,0.03083117],"category_scores_gemma":[0.002514702,0.004376407,0.003167641,0.005437037,0.003991083,0.003180483,0.002736483,0.004002503,0.01924896],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004755462,"about_ca_system_score_gemma":0.008377374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005887182,"about_ca_topic_score_gemma":0.007960668,"domain_scores_codex":[0.9731365,0.003207623,0.005260755,0.006076991,0.005219858,0.007098234],"domain_scores_gemma":[0.9844595,0.002929115,0.002806717,0.004345844,0.001863124,0.003595726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006691342,0.00550347,0.05274436,0.0009721566,0.004555968,0.004349393,0.04758596,0.0003899428,0.001547404,0.093797,0.7075332,0.08035198],"study_design_scores_gemma":[0.00548244,0.0004763469,0.02364824,0.001055762,0.0009804523,0.0003358967,0.02637355,0.0002837605,0.00188652,0.01241339,0.9211361,0.00592751],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2528177,0.0182964,0.0008426514,0.05603844,0.0269224,0.003681152,0.001275407,0.00264348,0.6374824],"genre_scores_gemma":[0.6079921,0.009096376,0.004697337,0.007594661,0.008730769,0.0006187483,0.001493681,0.000954242,0.3588221],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.3551744,"threshold_uncertainty_score":0.9997967,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03660702856669412,"score_gpt":0.3056469381663494,"score_spread":0.2690399095996552,"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."}}