{"id":"W3018065504","doi":"10.1017/nie.2020.14","title":"US AND UK LABOUR MARKETS BEFORE AND DURING THE COVID-19 CRASH","year":2020,"lang":"en","type":"article","venue":"National Institute Economic Review","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Economic and Social Research Council","keywords":"Quarter (Canadian coin); Unemployment; Coronavirus disease 2019 (COVID-19); Crash; Demographic economics; Recession; Shock (circulatory); Economics; Work (physics); Great recession; China; Labour economics; Political science; Economic growth; Geography; Medicine; Law; Keynesian economics","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":[0.00171331,0.0002806241,0.000614039,0.00242377,0.0007150566,0.001894151,0.000424863,0.00128625,0.005268666],"category_scores_gemma":[0.009618823,0.0002270079,0.0006063652,0.005785337,0.000658791,0.001497417,0.001624895,0.001352103,0.001331158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004479811,"about_ca_system_score_gemma":0.002879702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.3360938,"about_ca_topic_score_gemma":0.412091,"domain_scores_codex":[0.9982091,0.0003643102,0.0002573162,0.000172806,0.0003116698,0.0006847964],"domain_scores_gemma":[0.9958683,0.0007540712,0.001236069,0.0001244122,0.001455903,0.0005611886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004664504,0.0003106836,0.5162868,0.0101477,0.0008415884,0.00125003,0.01688881,0.002025031,0.0005387373,0.0126977,0.1797083,0.25464],"study_design_scores_gemma":[0.00004124371,0.0001946414,0.9415712,0.002472446,0.00007275557,0.00009831462,0.00485324,0.00008568132,0.00006990372,0.0001982106,0.05030872,0.00003368185],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7032043,0.2017952,0.0001133608,0.02228549,0.001249121,0.0001033455,0.04195308,0.00001642023,0.02927973],"genre_scores_gemma":[0.8756616,0.09267173,0.00009898339,0.00366865,0.0006073711,0.0001224212,0.02060318,0.00002322543,0.006542942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3360938,"threshold_uncertainty_score":0.6682751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08889163207508147,"score_gpt":0.4093786183467921,"score_spread":0.3204869862717106,"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."}}