{"id":"W4412865159","doi":"10.5430/wjel.v16n1p143","title":"Critical Analysis and Enhancement of Undergraduate English Syllabi: Aligning Pedagogical Deficiencies with Student and English Market Needs","year":2025,"lang":"en","type":"article","venue":"World Journal of English Language","topic":"Second Language Learning and Teaching","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"VIT University","keywords":"Syllabus; Curriculum; Competence (human resources); Medical education; Needs analysis; Job market; Mathematics education; Psychology; Pedagogy; Computer science; Medicine; Engineering","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":[0.02882427,0.000382391,0.0003729636,0.002350545,0.003804928,0.003977726,0.001175019,0.0006663828,0.001177142],"category_scores_gemma":[0.07619503,0.0002635475,0.0002602576,0.001031093,0.004496059,0.002119669,0.003637824,0.001089845,0.0001675056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007174728,"about_ca_system_score_gemma":0.01786941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004699344,"about_ca_topic_score_gemma":0.01226268,"domain_scores_codex":[0.9796953,0.01474485,0.0009773216,0.0005097739,0.003346138,0.0007266377],"domain_scores_gemma":[0.921426,0.04917005,0.005547825,0.001794357,0.02000453,0.002057232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001319881,0.0003411146,0.02716983,0.001482619,0.00001868148,0.0008832206,0.7469238,0.0004125882,0.009786306,0.01229717,0.006745168,0.1938075],"study_design_scores_gemma":[0.00001772287,0.0005035642,0.03410735,0.001396596,0.00004924617,0.0006341302,0.8829661,0.001416058,0.01259476,0.01081359,0.05539887,0.0001019318],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.948631,0.0006281424,0.02197469,0.006508839,0.0002608231,0.003087479,0.0001317562,0.00009042054,0.01868687],"genre_scores_gemma":[0.9600885,0.0007470023,0.03306378,0.0008664287,0.00004396787,0.001138587,0.00006883293,0.00003418048,0.003948695],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02882427,"threshold_uncertainty_score":0.1524391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01463626153258784,"score_gpt":0.279182279252972,"score_spread":0.2645460177203842,"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."}}