{"id":"W4213050332","doi":"10.18384/2224-0209-2022-1-1107","title":"MOBILIZATION MECHANISMS OF STATES IN THE POLICY OF COUNTERING THE COVID-19 PANDEMIC","year":2022,"lang":"en","type":"article","venue":"Bulletin of the Moscow State Regional University","topic":"Global Political and Economic Relations","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Foundation for Basic Research","keywords":"Pandemic; Political science; China; Politics; State (computer science); Kazakh; Population; Economic growth; Coronavirus disease 2019 (COVID-19); Mobilization; Development economics; Public administration; Economic history; Sociology; Law; History; Demography; Medicine","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":[0.01173282,0.0004272393,0.0004147807,0.003185106,0.004700569,0.008470229,0.0009981442,0.004214942,0.008445479],"category_scores_gemma":[0.01377428,0.0004380836,0.0005962336,0.001726331,0.01097106,0.004568085,0.007206563,0.002574181,0.0003772522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01023455,"about_ca_system_score_gemma":0.00844318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003959446,"about_ca_topic_score_gemma":0.004967065,"domain_scores_codex":[0.9921702,0.004753991,0.0002037506,0.0006194945,0.0005868187,0.001665812],"domain_scores_gemma":[0.99453,0.003547501,0.0007750723,0.0002205821,0.0003585294,0.0005682167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007413313,0.00004555195,0.001489693,0.0001022356,0.00002495365,0.0001121005,0.005222946,0.00114151,0.0003464956,0.9799733,0.002931585,0.008535414],"study_design_scores_gemma":[0.0002842434,0.0003034062,0.02001552,0.001223568,0.0001250529,0.000181228,0.03951432,0.006576814,0.001835178,0.7366168,0.1932138,0.0001101075],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3084766,0.005690742,0.01744033,0.1315293,0.0009845552,0.0007337247,0.0001467801,0.0001043843,0.5348936],"genre_scores_gemma":[0.9906681,0.0005994384,0.000814183,0.00174549,0.00008072868,0.0001878459,0.00001077637,0.00001013247,0.005883322],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01173282,"threshold_uncertainty_score":0.07425719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04131538651350355,"score_gpt":0.2709429317701614,"score_spread":0.2296275452566579,"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."}}