{"id":"W4308251732","doi":"10.5539/ijel.v12n6p98","title":"The Past, Present and Future of Machine Translation in China: A Visualization Study Based on CNKI Literature (1959-2021)","year":2022,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Machine translation; Computer science; Visualization; Competence (human resources); Discipline; China; Field (mathematics); Translation studies; Data science; Engineering ethics; Artificial intelligence; Natural language processing; Knowledge management; Political science; Engineering; Sociology; Linguistics; Management; Social science; Mathematics","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002602624,0.0003181196,0.0003145711,0.02284022,0.002214881,0.003227271,0.0004441866,0.0005024193,0.003115475],"category_scores_gemma":[0.005693741,0.0001722475,0.0003199424,0.03858389,0.001706707,0.003214499,0.001038266,0.0003742315,0.0003409567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006982528,"about_ca_system_score_gemma":0.008321711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05085703,"about_ca_topic_score_gemma":0.08713026,"domain_scores_codex":[0.9988538,0.0001860187,0.0002125639,0.0001517728,0.0004578429,0.0001380366],"domain_scores_gemma":[0.995797,0.001594347,0.001001085,0.0001998692,0.001183587,0.0002241047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003658395,0.00008779839,0.3067212,0.004329988,0.0001431738,0.007796156,0.1436067,0.001249226,0.008711743,0.04407982,0.01740987,0.4654985],"study_design_scores_gemma":[0.00001620673,0.0001082454,0.8054679,0.001573502,0.0001751769,0.002183378,0.04158336,0.001888691,0.00288715,0.003152275,0.1408738,0.00009032105],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9052752,0.04264696,0.001053659,0.004372225,0.0002358743,0.00006999054,0.002937793,0.00008835085,0.04331989],"genre_scores_gemma":[0.9758717,0.01572536,0.001240802,0.0001262303,0.0001225518,0.00004119478,0.00101198,0.00002050561,0.005839648],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9771598,"threshold_uncertainty_score":0.101122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01851454105519827,"score_gpt":0.2947196154323837,"score_spread":0.2762050743771854,"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."}}