{"id":"W4409793621","doi":"10.61091/jcmcc127a-182","title":"Research on Optimizing Vocational Education Curriculum System through Machine Learning to Enhance Students’ Employability","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Education and Vocational Training","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Education Department of Henan Province","keywords":"Employability; Vocational education; Curriculum; Mathematics education; Computer science; Engineering management; Medical education; Pedagogy; Engineering; Psychology; Medicine","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.008459103,0.0001796767,0.0004253606,0.0003909186,0.00154264,0.0004610815,0.0006227199,0.0001206476,0.00000979449],"category_scores_gemma":[0.002921261,0.0001767166,0.0001024736,0.00110695,0.000146093,0.0002286859,0.0001737943,0.0008138868,0.000008509369],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006553162,"about_ca_system_score_gemma":0.001456259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002301332,"about_ca_topic_score_gemma":0.000005025587,"domain_scores_codex":[0.9961407,0.0007731864,0.0009219001,0.0002783879,0.001524332,0.0003615339],"domain_scores_gemma":[0.9948634,0.002001613,0.0005320581,0.0001913446,0.002206486,0.0002050868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003416758,0.0009829267,0.00160075,0.0001328497,0.00004354001,9.105961e-7,0.01147167,0.0001035937,0.00002355419,0.9830672,0.0004475171,0.002091325],"study_design_scores_gemma":[0.00302807,0.001467258,0.003064389,0.006534672,0.0001431499,0.00001918827,0.1897499,0.001319244,0.0006328597,0.7552186,0.03799981,0.0008228687],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9377864,0.0003257689,0.00418602,0.002813417,0.02400596,0.0007432741,0.000001129846,0.00007619667,0.03006185],"genre_scores_gemma":[0.9908637,0.00003193189,0.007113691,0.00008539487,0.001747953,0.00001278446,0.000001943742,0.00001605282,0.0001265669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2278486,"threshold_uncertainty_score":0.9997572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04990038399616196,"score_gpt":0.4453871630984625,"score_spread":0.3954867791023006,"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."}}