{"id":"W4286376304","doi":"10.32983/2222-0712-2022-1-59-69","title":"The Impact of the COVID-19 Pandemic on Regional Labor Markets in Ukraine","year":2022,"lang":"en","type":"article","venue":"THE PROBLEMS OF ECONOMY","topic":"Labor Market and Education","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Strong; World Health Organization","keywords":"Pandemic; Quarter (Canadian coin); Population; Coronavirus disease 2019 (COVID-19); China; State (computer science); Quarantine; Development economics; Outbreak; Economic growth; Business; Geography; Economics; Demography; Virology; Medicine; Sociology","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":[0.000354088,0.0001395694,0.0001883909,0.0007462573,0.000786536,0.001545137,0.0003274226,0.0002095805,0.001665368],"category_scores_gemma":[0.0005555893,0.00008349898,0.0002852099,0.0007852213,0.0004697673,0.0007311138,0.001305349,0.0002924797,0.000174799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002354644,"about_ca_system_score_gemma":0.002162196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06024044,"about_ca_topic_score_gemma":0.05851179,"domain_scores_codex":[0.9996992,0.00008191537,0.00001652436,0.00003967887,0.00003766215,0.0001249661],"domain_scores_gemma":[0.999799,0.0000233428,0.00006909175,0.000007215348,0.00005596096,0.00004535029],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002518414,0.0001373091,0.8866851,0.0003496755,0.0001536643,0.004683121,0.01281771,0.01008694,0.002160307,0.01944936,0.004775473,0.05844945],"study_design_scores_gemma":[0.000004732513,0.00008649656,0.9358479,0.0002274614,0.00004503386,0.0005649048,0.03908766,0.005934575,0.0006959915,0.002054463,0.01543036,0.000020308],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9903547,0.0009740613,0.0003665976,0.0007304279,0.00002157302,0.00001582498,0.00051092,0.000007223548,0.007018604],"genre_scores_gemma":[0.9984872,0.0004955389,0.0001255737,0.00005997019,0.000006730015,0.000005562865,0.0001290813,0.000003643265,0.0006866992],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06024044,"threshold_uncertainty_score":0.1197796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06043144601834292,"score_gpt":0.271340571964534,"score_spread":0.2109091259461911,"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."}}