{"id":"W4362583651","doi":"10.3390/vaccines11040782","title":"Understanding the COVID-19 Vaccine Policy Terrain in Ontario Canada: A Policy Analysis of the Actors, Content, Processes, and Context","year":2023,"lang":"en","type":"article","venue":"Vaccines","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"British Academy","keywords":"Context (archaeology); Scarcity; Government (linguistics); Political science; Pandemic; Public policy; Public relations; Public administration; Business; Coronavirus disease 2019 (COVID-19); Geography; Medicine; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006101348,0.0001498885,0.0003535021,0.000541995,0.0005858036,0.00006569806,0.0004262995,0.00006799357,0.0001215863],"category_scores_gemma":[0.002592859,0.00009143982,0.00008575924,0.005576782,0.00002261851,0.0001443549,0.0001200821,0.0001595147,6.804782e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001663994,"about_ca_system_score_gemma":0.008542779,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9975986,"about_ca_topic_score_gemma":0.9999672,"domain_scores_codex":[0.9985195,0.0001864006,0.0003390217,0.0002394671,0.0003150686,0.0004005293],"domain_scores_gemma":[0.9986885,0.0006214747,0.0001895599,0.000267177,0.00008932744,0.0001439322],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003526534,0.00001531032,0.9425793,0.00003150088,0.0001868985,0.000006576407,0.03325363,0.00007157599,0.00002682016,0.01226565,0.01140117,0.0001262505],"study_design_scores_gemma":[0.0006891902,0.00001682833,0.938471,0.00002635011,0.0001574592,0.000001560028,0.02899572,0.00003677966,0.00001121479,0.002641584,0.02880309,0.0001491968],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8732492,0.0003192756,0.00003777921,0.1248795,0.00009951775,0.0003977633,0.00003315195,0.00003651542,0.0009473767],"genre_scores_gemma":[0.9938487,0.0002445634,0.000001173232,0.003190145,0.0001462212,0.00001627727,0.000008402819,0.000009805677,0.002534709],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1216893,"threshold_uncertainty_score":0.9970779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1275107481731029,"score_gpt":0.3247433674524294,"score_spread":0.1972326192793265,"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."}}