{"id":"W1965788119","doi":"10.7202/019894ar","title":"Training Human Translators as Opposed to Programming Machine Translation Systems: A Performative Model","year":2009,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Performative utterance; Computer science; Machine translation; Translation (biology); Computer-assisted translation; Example-based machine translation; Artificial intelligence; Training (meteorology); Natural language processing; Translation studies; Linguistics; 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":[0.01198554,0.0008433347,0.0006403414,0.0007061546,0.001302486,0.004091647,0.001617273,0.001783021,0.003959215],"category_scores_gemma":[0.02401876,0.0008029799,0.0005701896,0.0008154883,0.002544928,0.005295933,0.001970843,0.001893336,0.001744356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007908556,"about_ca_system_score_gemma":0.002105772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001625116,"about_ca_topic_score_gemma":0.002379111,"domain_scores_codex":[0.9899542,0.007384711,0.0003141546,0.001216231,0.0008389553,0.0002917205],"domain_scores_gemma":[0.9859876,0.01014898,0.00073937,0.001611323,0.001208728,0.0003039787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001008588,0.0009164318,0.01144745,0.001023244,0.000255955,0.0003813827,0.01769937,0.2754034,0.03132401,0.1621925,0.005149566,0.4931981],"study_design_scores_gemma":[0.0001422263,0.0008475165,0.001570905,0.000209672,0.000166603,0.0002564065,0.002599562,0.8355042,0.02277994,0.1173468,0.0184863,0.00008993945],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05901289,0.0001719939,0.9245795,0.002030365,0.0000721517,0.0002289408,0.00004137223,0.0008785476,0.01298428],"genre_scores_gemma":[0.6204293,0.0003250562,0.3685775,0.0006300788,0.0001367657,0.0004076811,0.0001548787,0.0003060291,0.009032699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01198554,"threshold_uncertainty_score":0.06338638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1781235596816631,"score_gpt":0.3272697898715288,"score_spread":0.1491462301898657,"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."}}