{"id":"W4361280208","doi":"10.5430/wjel.v13n5p177","title":"English-Arabic Translation of COVID-19 Prevention and Control Terminology: A Domesticating Approach","year":2023,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Discourse Analysis and Cultural Communication","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Terminology; CLARITY; Linguistics; Context (archaeology); Arabic; Coronavirus disease 2019 (COVID-19); Control (management); Computer science; Quality (philosophy); Naturalness; Natural language processing; Artificial intelligence; History; Medicine","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":[0.007096872,0.0007994632,0.0003924574,0.001316971,0.001825722,0.002252512,0.0005316837,0.0005382621,0.008723053],"category_scores_gemma":[0.01654093,0.0001945564,0.0002677873,0.001865916,0.001094682,0.001634509,0.001650756,0.001301986,0.003158004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001414721,"about_ca_system_score_gemma":0.003381036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00178547,"about_ca_topic_score_gemma":0.002148646,"domain_scores_codex":[0.9949085,0.003451247,0.0005918269,0.0001802072,0.0007365345,0.0001317366],"domain_scores_gemma":[0.9874685,0.005462708,0.001033537,0.0005845852,0.00514059,0.0003100835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001248874,0.0007773167,0.01718647,0.006757417,0.00005388736,0.005544943,0.3517047,0.001070963,0.03950021,0.05550469,0.04511612,0.4755344],"study_design_scores_gemma":[0.0001382506,0.001050142,0.02743936,0.004076473,0.0001286,0.004539179,0.2634691,0.00476602,0.02668979,0.01155378,0.6559895,0.0001596794],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6593507,0.007016315,0.08157525,0.01956889,0.003969226,0.003493659,0.002928365,0.0004506249,0.2216469],"genre_scores_gemma":[0.8340562,0.006186795,0.1346664,0.002295654,0.0003786712,0.001797046,0.001610026,0.0002318135,0.0187773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008723053,"threshold_uncertainty_score":0.03753227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0344405324542464,"score_gpt":0.3496872679738552,"score_spread":0.3152467355196088,"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."}}