{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001731843,0.0004234407,0.0002965794,0.0025971,0.00153572,0.003594443,0.0006178233,0.0009524993,0.01343176],"category_scores_gemma":[0.004298735,0.0005198761,0.0004533398,0.001837781,0.002813002,0.001892719,0.001523237,0.001473495,0.005315126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001461937,"about_ca_system_score_gemma":0.00357405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004244399,"about_ca_topic_score_gemma":0.006492399,"domain_scores_codex":[0.9977461,0.0004454892,0.0001306292,0.0003417449,0.001169079,0.0001670738],"domain_scores_gemma":[0.9975536,0.0008068183,0.0004843285,0.0004030659,0.0005932567,0.0001588107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001072902,0.00007890394,0.004876235,0.0006191408,0.00003771672,0.001369383,0.007030235,0.001248334,0.01460291,0.4029455,0.01195646,0.555128],"study_design_scores_gemma":[0.00002886099,0.0001391729,0.008785727,0.0004518573,0.00006327865,0.003198913,0.003177234,0.001019422,0.01223869,0.09434771,0.8764485,0.0001006608],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09980416,0.06565356,0.1843978,0.01092014,0.00285284,0.0004534282,0.001027684,0.0006688535,0.6342216],"genre_scores_gemma":[0.7045462,0.04893904,0.1482371,0.0007012985,0.001199023,0.0006444431,0.0005167546,0.0003189194,0.09489723],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01343176,"threshold_uncertainty_score":0.04493368,"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."}}