{"id":"W2615268326","doi":"10.5539/jel.v6n4p12","title":"Analyzing Upper Secondary Education Dropout in Latin America through a Cohort Approach","year":2017,"lang":"en","type":"article","venue":"Journal of Education and Learning","topic":"Poverty, Education, and Child Welfare","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Socioeconomic status; Dropout (neural networks); Latin Americans; Demographic economics; Cohort; Demography; Economics; Secondary education; Population; Economic growth; Political science; Psychology; Sociology; Medicine; Mathematics education","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":[0.003852151,0.000266862,0.0003372472,0.001685239,0.0009589819,0.000934158,0.0007419378,0.0003683337,0.001683387],"category_scores_gemma":[0.00430532,0.0002440089,0.0008360549,0.001656643,0.00023211,0.0004546157,0.001287424,0.0004984732,0.0003011893],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006845284,"about_ca_system_score_gemma":0.001300772,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1031833,"about_ca_topic_score_gemma":0.0881303,"domain_scores_codex":[0.9991453,0.0003760954,0.00004441958,0.0001337251,0.00008879218,0.0002117618],"domain_scores_gemma":[0.9979766,0.0003626296,0.0006708981,0.0004545656,0.0002808531,0.0002544105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005549552,0.00004728954,0.9959053,0.000005981742,0.00005949362,0.00004421684,0.0003961042,0.00006916476,0.00007276892,0.00009419477,0.0001371164,0.003112827],"study_design_scores_gemma":[0.000009892798,0.0001758638,0.9944699,0.00003166027,0.00007098781,0.00006645052,0.002737511,0.0007900179,0.00008864393,0.000169178,0.001384333,0.000005595794],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966124,0.0001580485,0.001235243,0.0001021167,0.00001113657,0.00006804671,0.001096033,0.00001106484,0.000705802],"genre_scores_gemma":[0.9959561,0.0002504697,0.0009534834,0.00008096647,0.00001098948,0.0001472744,0.001497382,0.000007457867,0.001095705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1031833,"threshold_uncertainty_score":0.2051654,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01566621970263063,"score_gpt":0.3247282368480538,"score_spread":0.3090620171454231,"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."}}