{"id":"W3038709602","doi":"10.1080/14494035.2020.1787628","title":"Mobilizing Policy (In)Capacity to Fight COVID-19: Understanding Variations in State Responses","year":2020,"lang":"en","type":"article","venue":"Policy and Society","topic":"Disaster Management and Resilience","field":"Social Sciences","cited_by":576,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Operationalization; Pandemic; Government (linguistics); Politics; Political science; Coronavirus disease 2019 (COVID-19); Public policy; Population; State (computer science); Public relations; Public administration; Economic growth; Sociology; Economics; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006317066,0.00008165802,0.000117711,0.000112554,0.0004525774,0.0001241333,0.0001589418,0.00005161787,0.00002279197],"category_scores_gemma":[0.00149962,0.00008241126,0.00004876126,0.001275809,0.0001940603,0.0002242745,0.00009538964,0.0001054967,0.000006287152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005265102,"about_ca_system_score_gemma":0.000391098,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01676782,"about_ca_topic_score_gemma":0.004945155,"domain_scores_codex":[0.9988726,0.0001924288,0.0001591699,0.0002271467,0.0001924262,0.0003562475],"domain_scores_gemma":[0.9992721,0.000284798,0.00003587923,0.0000702283,0.000008653516,0.0003283311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001915094,0.00002568794,0.01078354,0.00003960589,0.000007630559,0.00000220511,0.5303121,0.0002723193,0.0001035085,0.456598,0.001631514,0.0002046794],"study_design_scores_gemma":[0.002246428,0.0001586042,0.09919871,0.0001093927,0.00002088335,7.213193e-7,0.3547408,0.003559924,0.00003802169,0.3017187,0.2372612,0.0009466891],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6215104,0.00005823743,0.01086258,0.3101375,0.00008139312,0.0008087934,0.00004872664,0.0001409747,0.05635135],"genre_scores_gemma":[0.9793544,0.0001794807,0.0003235021,0.01918978,0.0001737925,0.00001300898,7.368189e-7,0.000005011552,0.000760296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3578439,"threshold_uncertainty_score":0.9897796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1494008938866348,"score_gpt":0.388746931501367,"score_spread":0.2393460376147321,"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."}}