{"id":"W4413198651","doi":"10.3102/ip.25.2195942","title":"Apprenticeship, Disability, and Earnings: A Canadian Case Study Involving Quantitative Analysis of Administrative Data (Poster 37)","year":2025,"lang":"en","type":"article","venue":"","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Earnings; Apprenticeship; Computer science; Accounting; Business; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.007793634,0.0004903051,0.0005486269,0.005008804,0.01146206,0.001931948,0.001782958,0.001131626,0.00208544],"category_scores_gemma":[0.0132523,0.000422254,0.0009392939,0.009672491,0.002585375,0.0008531871,0.002308837,0.001681715,0.000154163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04185509,"about_ca_system_score_gemma":0.06300942,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9896124,"about_ca_topic_score_gemma":0.9941077,"domain_scores_codex":[0.9937412,0.002296143,0.0002065126,0.00028255,0.001652757,0.001820746],"domain_scores_gemma":[0.9931768,0.002141017,0.0006455285,0.0002838543,0.003160689,0.0005921263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003644637,0.001098112,0.8248668,0.0003614395,0.0001472412,0.005553775,0.0987846,0.0006485381,0.0007221806,0.006838375,0.01070693,0.04990755],"study_design_scores_gemma":[0.00004605262,0.0002953377,0.7350844,0.0004873109,0.0001730009,0.001632363,0.2492287,0.001534055,0.0005247319,0.0008231332,0.01006117,0.0001096572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845204,0.001309357,0.0007162814,0.002362442,0.00003982687,0.00047992,0.00173208,0.0000112526,0.008828498],"genre_scores_gemma":[0.9920596,0.00137175,0.002213035,0.0003564515,0.00001728313,0.0002083186,0.0005832823,0.00001074733,0.003179451],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04185509,"threshold_uncertainty_score":0.3036814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4443217940576979,"score_gpt":0.5203005668920273,"score_spread":0.07597877283432936,"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."}}