{"id":"W3169272657","doi":"10.1177/00438200211052045","title":"HEALTH VULNERABILITY VERSUS ECONOMIC RESILIENCE TO THE COVID-19 PANDEMIC","year":2021,"lang":"en","type":"article","venue":"World Affairs","topic":"Economic Growth and Development","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Vulnerability (computing); China; Resilience (materials science); Geography; Vulnerability index; Quadrant (abdomen); Index (typography); Economic growth; Socioeconomics; Development economics; Political science; Medicine; Economics; Biology; Infectious disease (medical specialty)","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":[],"consensus_categories":[],"category_scores_codex":[0.0009888253,0.0001308757,0.0001954084,0.00007564923,0.0003401109,0.0001456777,0.0009182547,0.00002213716,0.000157662],"category_scores_gemma":[0.0001888468,0.0001089858,0.0000619904,0.0003419822,0.00005000133,0.0001736652,0.0004743895,0.0001529045,0.0006883605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009816689,"about_ca_system_score_gemma":0.001993934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002192352,"about_ca_topic_score_gemma":0.01728488,"domain_scores_codex":[0.9983633,0.0001629714,0.0003193069,0.0006266028,0.0001117644,0.0004160755],"domain_scores_gemma":[0.9980816,0.0004047592,0.0000865309,0.0009124946,0.00002079343,0.0004938777],"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":[0.0001409814,0.0001240604,0.1001243,0.00007702637,0.00006875895,0.00005900235,0.007773214,0.01855982,0.00003064844,0.274137,0.4824528,0.1164524],"study_design_scores_gemma":[0.001062857,0.00008216184,0.01541719,0.0000122924,0.000002900084,0.00007931324,0.0006988195,0.003782878,0.0001361057,0.00326124,0.9750373,0.0004269236],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07219701,0.002043591,0.1897264,0.6114991,0.01079921,0.001304127,0.00003856421,0.000853923,0.1115382],"genre_scores_gemma":[0.9791088,0.00002650825,0.008517276,0.009653646,0.0001005795,0.00003874527,0.000003421177,0.000006076384,0.002545005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9069117,"threshold_uncertainty_score":0.9645369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04575326491327701,"score_gpt":0.306673224878468,"score_spread":0.260919959965191,"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."}}