{"id":"W4391772215","doi":"10.1080/13572334.2024.2313310","title":"Governments and parliaments in a state of emergency: what can we learn from the COVID-19 pandemic?","year":2024,"lang":"en","type":"article","venue":"Journal of Legislative Studies","topic":"Socio-political and Technological Issues","field":"Social Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); State of emergency; Political science; 2019-20 coronavirus outbreak; State (computer science); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public administration; Virology; Medicine; Law; Computer science; Politics; Outbreak","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":[0.0007223174,0.00008735776,0.0002745699,0.00003601623,0.0001628435,0.00004495932,0.0001560317,0.00004252565,0.00004854294],"category_scores_gemma":[0.001590368,0.00004816256,0.0000686981,0.0002187898,0.0006616811,0.000277102,0.00009606027,0.0002574294,0.000001526996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002432726,"about_ca_system_score_gemma":0.0000865621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002050851,"about_ca_topic_score_gemma":0.004855279,"domain_scores_codex":[0.9987003,0.0002356288,0.000340654,0.0001117038,0.0004288276,0.0001828382],"domain_scores_gemma":[0.9984789,0.001150602,0.0001627277,0.00004731771,0.00008402021,0.00007639945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004126178,0.00006608223,0.4920374,0.00004383721,0.0007020827,0.00006480821,0.4656787,0.000004167426,0.00007887568,0.007933979,0.001955347,0.03139345],"study_design_scores_gemma":[0.000352631,0.0001821394,0.02260215,0.0003944569,0.00005765519,0.000001132405,0.3716174,0.000005351591,0.00003323283,0.5046807,0.09995658,0.0001165825],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8685444,0.08904493,0.00002584365,0.04082309,0.0005545064,0.0001242843,0.00002862736,0.00001729194,0.0008370022],"genre_scores_gemma":[0.8869858,0.1122007,0.00003831998,0.0001798704,0.0001093891,0.000003209175,1.207982e-7,0.000003276032,0.0004793161],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4967467,"threshold_uncertainty_score":0.3100286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1630664570721894,"score_gpt":0.4247199404289856,"score_spread":0.2616534833567962,"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."}}