{"id":"W4392906147","doi":"10.32920/25412824.v1","title":"Performance of Language: A Comparative Linguistic Study of News About China and Italy During the COVID-19 Pandemic in a Canadian News Program","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Linguistic Studies and Language Acquisition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; Centre for Social Innovation; York University","funders":"","keywords":"Mainstream; China; Status quo; Ideology; Pandemic; Political science; Linguistics; Situated; Animation; News media; Coronavirus disease 2019 (COVID-19); Sociology; History; Media studies; Computer science; Politics; Medicine; Artificial intelligence","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":[0.0003997109,0.0002513401,0.0005103881,0.0002762833,0.0001136625,0.0001061126,0.0006631996,0.00008740759,0.00001320961],"category_scores_gemma":[0.0003340869,0.0001706695,0.00005944007,0.0003981101,0.0001008583,0.00002499114,0.001291196,0.0004700849,0.000001674099],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002281317,"about_ca_system_score_gemma":0.0005416544,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7342026,"about_ca_topic_score_gemma":0.701754,"domain_scores_codex":[0.9982641,0.0001090992,0.000520965,0.0005315215,0.0002490774,0.0003252846],"domain_scores_gemma":[0.9987603,0.0001391696,0.0002155029,0.0006228712,0.00009678061,0.0001654098],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00004967022,0.0003479495,0.2831982,0.002174757,0.000275037,0.0002563899,0.7031357,0.001896686,0.00001733081,0.001129374,0.0001226821,0.00739621],"study_design_scores_gemma":[0.003114612,0.001817125,0.6992853,0.001342264,0.0003115596,0.0001274137,0.06870782,0.2213058,0.00004933087,0.001791167,0.0007942652,0.001353427],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9922172,0.00277855,0.000066154,0.0001333701,0.0003369261,0.001479913,0.00001960397,0.00007759202,0.00289071],"genre_scores_gemma":[0.9986799,0.00009101033,0.0006995873,0.0001246297,0.0001318713,0.000147564,0.000005254888,0.000009687102,0.000110442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6344279,"threshold_uncertainty_score":0.6959702,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02742687833251391,"score_gpt":0.3322569939953815,"score_spread":0.3048301156628676,"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."}}