{"id":"W4293095831","doi":"10.5539/elt.v15n9p106","title":"Code Switching and Code Mixing in Teaching and Learning of English as a Second Language: Building on Knowledge","year":2022,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Second Language Learning and Teaching","field":"Arts and Humanities","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Code-switching; Code-mixing; Code (set theory); Mixing (physics); Psychology; Process (computing); Task (project management); Mathematics education; Linguistics; First language; Computer science; Language assessment; Programming language; Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.005195944,0.0004270892,0.0006048873,0.0007154323,0.002219277,0.0003901294,0.0003450015,0.00008966831,0.00100287],"category_scores_gemma":[0.005322792,0.0004490165,0.0001139904,0.00008868502,0.0001016999,0.0005056947,0.000492856,0.006099173,0.000002879981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001534008,"about_ca_system_score_gemma":0.00004346919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002960691,"about_ca_topic_score_gemma":0.002082479,"domain_scores_codex":[0.9955184,0.002145105,0.0006067802,0.0007260198,0.000375038,0.0006286818],"domain_scores_gemma":[0.997364,0.001771892,0.0003375752,0.0003343945,0.0000481132,0.000143959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004786862,0.0001249842,0.001205107,0.0001580579,0.00005351918,0.0001044318,0.9082134,0.0003689981,0.002415554,0.0218056,0.0000651544,0.06543736],"study_design_scores_gemma":[0.001436713,0.0003215413,0.0001070641,0.0003753193,0.00004568456,0.00002700399,0.9306068,0.0023221,0.000316139,0.00005925264,0.06376068,0.0006217345],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.906557,0.00382359,0.00003556448,0.00002918509,0.0007340701,0.0002358541,0.00004402602,0.0003973862,0.0881433],"genre_scores_gemma":[0.993652,0.000004981811,0.0006036843,0.0003027951,0.001322087,0.00003782591,0.00004064722,0.000117919,0.003917987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08709504,"threshold_uncertainty_score":0.9999104,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01014276641962376,"score_gpt":0.2532036374938079,"score_spread":0.2430608710741841,"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."}}