{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00409792,0.0001028515,0.0003790484,0.001108155,0.002534916,0.005107307,0.0005040281,0.001345689,0.004001527],"category_scores_gemma":[0.0170575,0.0002203549,0.0002410767,0.001801169,0.006424924,0.007447551,0.002695071,0.002764835,0.0003678667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002400844,"about_ca_system_score_gemma":0.001968121,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01317258,"about_ca_topic_score_gemma":0.02867262,"domain_scores_codex":[0.9963952,0.001879437,0.0001253729,0.0003363125,0.0003379754,0.0009257202],"domain_scores_gemma":[0.9850464,0.006971062,0.004636263,0.0008365845,0.0009750196,0.001534673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000268878,0.0002070011,0.5259191,0.0002413165,0.00008893927,0.001095957,0.3494255,0.0007685415,0.0006234764,0.06254927,0.005226665,0.05358531],"study_design_scores_gemma":[0.000008530916,0.00008649666,0.6172302,0.0002672627,0.00002123481,0.0003055979,0.3391531,0.0003634592,0.0002853866,0.01097478,0.03126055,0.00004341393],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9624208,0.001246707,0.0004389831,0.01653965,0.00007869444,0.000008271512,0.00009462066,0.000005420055,0.01916685],"genre_scores_gemma":[0.9981841,0.0004991424,0.00008054852,0.0005500696,0.00003716884,0.0000036095,0.00005409819,0.0000046397,0.0005864695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01317258,"threshold_uncertainty_score":0.02619177,"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."}}