{"id":"W4403425734","doi":"10.1080/07352166.2024.2407358","title":"Governing pandemics: Resilience and community responses for COVID-19 in Bengaluru and Shanghai","year":2024,"lang":"en","type":"article","venue":"Journal of Urban Affairs","topic":"COVID-19 epidemiological studies","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Pandemic; Coronavirus disease 2019 (COVID-19); Resilience (materials science); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Community resilience; Geography; Political science; Economic growth; Socioeconomics; Sociology; Virology; Economics; Outbreak; Biology; Medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006759426,0.0001419444,0.0004516134,0.0001482974,0.0002450724,0.00005577779,0.0001810783,0.00009409832,0.000007428091],"category_scores_gemma":[0.05072558,0.0001030504,0.00008625176,0.0001571898,0.0002067342,0.0001618383,0.0001800791,0.0007630759,5.595374e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000299373,"about_ca_system_score_gemma":0.0001439662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007514453,"about_ca_topic_score_gemma":0.0003548751,"domain_scores_codex":[0.9981757,0.000709847,0.0005749428,0.0001480914,0.0001766891,0.0002146738],"domain_scores_gemma":[0.9684461,0.03097173,0.000234536,0.0001332067,0.00005425169,0.0001601446],"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":[0.003350179,0.0005563819,0.6472904,0.006918168,0.0005514904,0.0005423941,0.05467356,0.0002255744,0.00247318,0.0543082,0.2167459,0.01236455],"study_design_scores_gemma":[0.003933623,0.003127908,0.09102758,0.002530367,0.000355644,0.0006359204,0.0549057,0.005548676,0.0001033016,0.6648675,0.1720843,0.0008795004],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.970432,0.01326357,0.007598961,0.007808928,0.0001446391,0.0002924861,0.00004802104,0.00005397134,0.0003574163],"genre_scores_gemma":[0.9933046,0.0009306115,0.004996631,0.0004423702,0.00009709746,0.000007855499,4.236818e-7,0.00001353418,0.0002068952],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6105593,"threshold_uncertainty_score":0.9572706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2360764346948292,"score_gpt":0.4353906613707347,"score_spread":0.1993142266759056,"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."}}