{"id":"W2048533832","doi":"10.7202/1026474ar","title":"On Describing Similarity and Measuring Equivalence in English-Spanish Translation","year":2014,"lang":"en","type":"article","venue":"Meta Journal des traducteurs","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Equivalence (formal languages); Participle; Linguistics; Source text; Computer science; Natural language processing; Grading (engineering); Similarity (geometry); Dynamic and formal equivalence; Target text; Contrastive analysis; Artificial intelligence; Machine translation; Philosophy; Verb","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":[0.0213259,0.0006573494,0.0007246099,0.01441992,0.002221134,0.004888343,0.001067213,0.001299654,0.003372656],"category_scores_gemma":[0.1114974,0.0002465509,0.0008556389,0.01662276,0.006558788,0.008210272,0.004630228,0.001227564,0.0005187931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002690593,"about_ca_system_score_gemma":0.001940678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002549117,"about_ca_topic_score_gemma":0.002184542,"domain_scores_codex":[0.9630145,0.02156992,0.004369766,0.002234868,0.00812005,0.0006908134],"domain_scores_gemma":[0.8984166,0.07300023,0.007318927,0.006929737,0.01368192,0.0006526203],"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":[0.0008049053,0.0004853214,0.1189611,0.002810356,0.0003316151,0.0006713115,0.1604698,0.003898237,0.01307025,0.2574271,0.002245181,0.4388248],"study_design_scores_gemma":[0.0001107117,0.001840249,0.3022429,0.002129354,0.0003677414,0.002363851,0.2117872,0.02401933,0.0177798,0.3461224,0.09086709,0.0003694364],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6607953,0.003276641,0.2130286,0.0008448472,0.0002084585,0.001386633,0.001080426,0.0003415337,0.1190376],"genre_scores_gemma":[0.9481618,0.0005705106,0.04831704,0.00007737467,0.00005882701,0.0007128241,0.0008261228,0.00008002296,0.001195565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0213259,"threshold_uncertainty_score":0.1127835,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1978731705976733,"score_gpt":0.2739919164281739,"score_spread":0.0761187458305006,"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."}}