{"id":"W4385394818","doi":"10.2196/41448","title":"Examining Public Awareness of Ageist Terms on Twitter: Content Analysis","year":2023,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Aging and Gerontology Research","field":"Psychology","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Content analysis; Vulnerability (computing); Social media; Public discourse; Psychology; Discourse analysis; Public health; Situated; Population; Elderly people; Gerontology; Medicine; Disease; Coronavirus disease 2019 (COVID-19); Sociology; Nursing; Political science; Linguistics; Social science; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003788806,0.000307656,0.0004065417,0.005560509,0.00138052,0.00268228,0.0005169035,0.000713421,0.00190996],"category_scores_gemma":[0.02245061,0.0002282885,0.0003383089,0.005835621,0.001217133,0.003876107,0.002565546,0.0007396699,0.0005683023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001754325,"about_ca_system_score_gemma":0.001135075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004014444,"about_ca_topic_score_gemma":0.004786098,"domain_scores_codex":[0.9969895,0.001387042,0.0003060249,0.0002957621,0.0007312304,0.0002903473],"domain_scores_gemma":[0.9741768,0.0190652,0.003169026,0.0005427337,0.00261926,0.0004270648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004678403,0.0002404111,0.2909481,0.004037656,0.0001213099,0.001284898,0.5879411,0.0004813193,0.009258973,0.003883959,0.01121648,0.09011804],"study_design_scores_gemma":[0.00002390295,0.0001857626,0.4181758,0.001273495,0.000161038,0.0004944376,0.517229,0.004501547,0.003584619,0.001934221,0.0523021,0.0001340715],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889174,0.0002926473,0.000850639,0.001025168,0.00004348475,0.0003229668,0.003355369,0.00003099691,0.005161401],"genre_scores_gemma":[0.9897069,0.0007781251,0.003034105,0.0005305191,0.0001137443,0.001109104,0.00295205,0.00005438123,0.001721138],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005560509,"threshold_uncertainty_score":0.02003735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3431930188803787,"score_gpt":0.4487368141691396,"score_spread":0.1055437952887608,"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."}}