{"id":"W3110302913","doi":"10.1016/j.socscimed.2020.113549","title":"Open government data, uncertainty and coronavirus: An infodemiological case study","year":2020,"lang":"en","type":"article","venue":"Social Science & Medicine","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; Public Health Ontario; University of Toronto; McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Transparency (behavior); Pandemic; Accountability; Government (linguistics); Open data; Open government; Public health; Population; Public relations; Value (mathematics); Business; Coronavirus disease 2019 (COVID-19); Political science; Actuarial science; Public economics; Medicine; Environmental health; Economics; Computer science; Law; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005951608,0.0002688166,0.0003238703,0.00219994,0.004835042,0.003386449,0.001000417,0.004155632,0.004003509],"category_scores_gemma":[0.03091164,0.0002289262,0.0005254248,0.003721586,0.003049124,0.003135077,0.00234934,0.002305606,0.0002679027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003069163,"about_ca_system_score_gemma":0.002591052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03717044,"about_ca_topic_score_gemma":0.03321974,"domain_scores_codex":[0.995702,0.002613096,0.0002521302,0.0001985079,0.0006684376,0.0005658581],"domain_scores_gemma":[0.9530913,0.03922753,0.002833251,0.001500555,0.001763416,0.001583908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001308139,0.003489229,0.5745431,0.0008613787,0.0002839504,0.1049338,0.06533987,0.01094335,0.0008582622,0.132721,0.02936615,0.0753518],"study_design_scores_gemma":[0.0004877608,0.001668605,0.1665819,0.001787386,0.0004400846,0.1389305,0.388821,0.04951027,0.003502781,0.09381829,0.1541655,0.0002859744],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9543112,0.001176621,0.003854078,0.01693153,0.00006983566,0.0001873895,0.001063103,0.00002831595,0.02237792],"genre_scores_gemma":[0.9953948,0.0006306796,0.001915963,0.0006526734,0.0000513266,0.00004529682,0.0002274668,0.00001251806,0.001069166],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03717044,"threshold_uncertainty_score":0.07390815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2051445018283466,"score_gpt":0.4515485926020226,"score_spread":0.2464040907736759,"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."}}