{"id":"W4394752997","doi":"10.5430/wjel.v14n4p254","title":"Reference in English-Chinese Legal Translation: Human Translators Versus ChatGPT","year":2024,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Literal translation; Cohesion (chemistry); Computer science; Linguistics; Demonstrative; Natural language processing; Translation (biology); Annotation; Meaning (existential); Artificial intelligence; Source text; Psychology; Philosophy","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":[0.01856332,0.0005716117,0.0006024279,0.002117502,0.005206885,0.005511249,0.001198536,0.001213273,0.005792577],"category_scores_gemma":[0.05055977,0.0002688786,0.000347362,0.004719131,0.01061361,0.006553751,0.004995477,0.001427396,0.0008627044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00452141,"about_ca_system_score_gemma":0.005273346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076401,"about_ca_topic_score_gemma":0.0112916,"domain_scores_codex":[0.9667244,0.02547882,0.001452743,0.001741286,0.003679742,0.0009230496],"domain_scores_gemma":[0.9564722,0.03132394,0.003238591,0.003004574,0.005092114,0.0008685822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001533945,0.00007008663,0.0112419,0.0008063791,0.00001918977,0.001770884,0.8964663,0.0001022352,0.001622263,0.03203013,0.001805288,0.05391192],"study_design_scores_gemma":[0.00007653755,0.0004026883,0.03525046,0.001962058,0.0001148774,0.002728843,0.8490912,0.001921058,0.007025674,0.01497158,0.08634116,0.0001139386],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8306198,0.003920383,0.02003933,0.006087782,0.0004226812,0.0005914994,0.0001837275,0.0001283882,0.1380064],"genre_scores_gemma":[0.9902028,0.0007549437,0.004104609,0.0004988912,0.00005327741,0.0002380084,0.00006393863,0.00004395606,0.004039645],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01856332,"threshold_uncertainty_score":0.09817344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03836036707416804,"score_gpt":0.3044691116391297,"score_spread":0.2661087445649617,"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."}}