{"id":"W4412636068","doi":"10.17975/sfj-2025-011","title":"A multivariate analysis using machine learning of the impact of education-based factors on employment income for youth","year":2025,"lang":"en","type":"article","venue":"STEM Fellowship Journal","topic":"Korean Urban and Social Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Olds College","funders":"","keywords":"Multivariate statistics; Multivariate analysis; Computer science; Mathematics education; Psychology; Demographic economics; Machine learning; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003341533,0.000106796,0.0002455855,0.0001043161,0.0003274792,0.00001724371,0.0001770388,0.00003298047,0.00007216218],"category_scores_gemma":[0.0000710863,0.00006300463,0.000476777,0.000545609,0.0000963331,0.00003375398,0.00004954911,0.0001446427,4.887945e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004144234,"about_ca_system_score_gemma":0.00009220019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00296696,"about_ca_topic_score_gemma":0.0001427176,"domain_scores_codex":[0.9991188,0.0001434377,0.000282763,0.0001032444,0.0002088975,0.0001428277],"domain_scores_gemma":[0.999357,0.0001387669,0.0003230342,0.0001090446,0.00002817901,0.00004397006],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000403976,0.00009916917,0.9460956,0.000005604727,0.0003508796,5.692671e-8,0.002858429,0.04944728,0.0006770228,0.00001742704,0.000011564,0.0003965347],"study_design_scores_gemma":[0.0004157092,0.0001185861,0.9831952,0.00008526349,0.0004307906,1.350561e-7,0.003092272,0.01080929,0.001597377,0.0001534487,0.00001379463,0.00008807977],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975629,0.00006048033,0.001440811,0.00002996232,0.0001278154,0.000151367,0.00002566934,0.000003749652,0.0005972575],"genre_scores_gemma":[0.9994203,0.000002664256,0.0001360352,0.00001537779,0.00001591973,0.000001562479,0.000002016133,0.000005841562,0.0004002693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03863798,"threshold_uncertainty_score":0.4485176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03483280162324449,"score_gpt":0.3138441287331984,"score_spread":0.279011327109954,"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."}}