{"id":"W4404367135","doi":"10.1016/j.shaw.2024.11.002","title":"Anti-masking Posts on Instagram: Content Analysis During the COVID-19 Pandemic","year":2024,"lang":"en","type":"article","venue":"Safety and Health at Work","topic":"Misinformation and Its Impacts","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Centre for Disease Control; University of Toronto; University of Calgary; Toronto Metropolitan University; University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Coronavirus disease 2019 (COVID-19); Pandemic; Masking (illustration); 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Content (measure theory); Environmental health; Internet privacy; Computer science; Virology; Medicine; Mathematics; Outbreak","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":[0.00495826,0.0003456817,0.0003652481,0.00393543,0.002205722,0.002539745,0.0006347253,0.0008305419,0.00305995],"category_scores_gemma":[0.02350093,0.0002666062,0.0002805906,0.004734544,0.002018925,0.002744777,0.003114401,0.0009295307,0.0007092796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002763057,"about_ca_system_score_gemma":0.00202811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008818375,"about_ca_topic_score_gemma":0.01672432,"domain_scores_codex":[0.9963595,0.001817829,0.0002651576,0.0003686375,0.0006797562,0.0005091559],"domain_scores_gemma":[0.9756536,0.01732676,0.002655369,0.0005746403,0.003197789,0.0005918359],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0003827974,0.00009967951,0.04291204,0.001449837,0.00002100837,0.0008179239,0.8940704,0.0001553138,0.006844783,0.001468156,0.009155576,0.04262244],"study_design_scores_gemma":[0.0000136176,0.0001202853,0.1812827,0.000638578,0.00003578276,0.0002254038,0.7831701,0.00115802,0.002087335,0.0005892506,0.03060757,0.0000712266],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895045,0.0001504564,0.0008830574,0.001086887,0.00006006823,0.0003857807,0.003310073,0.00004132656,0.00457787],"genre_scores_gemma":[0.9845953,0.0004697812,0.004798774,0.0006318683,0.0001070957,0.001723232,0.003274921,0.0001348892,0.004264195],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008818375,"threshold_uncertainty_score":0.02622211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1540554146122338,"score_gpt":0.4096997491901202,"score_spread":0.2556443345778864,"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."}}