{"id":"W4408140443","doi":"10.1093/ooec/odae025","title":"Origins of Latin American inequality","year":2025,"lang":"en","type":"article","venue":"Oxford Open Economics","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inequality; Latin Americans; Mathematics; Political science; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217388,0.0000853875,0.0003244024,0.00005096031,0.0002360846,0.0001121266,0.0007425604,0.00005366993,0.000172808],"category_scores_gemma":[0.0001954741,0.00008997149,0.00006870001,0.0002255122,0.0003140474,0.0003115568,0.0002272849,0.00008330934,0.00001676335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005724609,"about_ca_system_score_gemma":0.0005249419,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.05942013,"about_ca_topic_score_gemma":0.03003784,"domain_scores_codex":[0.9989721,0.0001495132,0.0003951891,0.0001986678,0.00005454143,0.0002300118],"domain_scores_gemma":[0.9991661,0.0001572806,0.0002463218,0.0003046232,0.00006222028,0.00006350696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002476092,0.00004877312,0.1224962,0.000008209841,0.00001885265,9.557427e-8,0.001029522,0.00001038315,0.000003211413,0.8639022,0.0004003335,0.01205742],"study_design_scores_gemma":[0.0002995828,0.00003812078,0.05300806,0.00001045185,0.00001031595,2.068515e-8,0.003725375,0.0001024828,0.0001781745,0.03187578,0.9106091,0.0001425169],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6342099,0.00000545966,0.0001629274,0.00106651,0.000294893,0.0001946549,0.0000368656,0.00001325907,0.3640155],"genre_scores_gemma":[0.9947408,0.0002918915,0.001181571,0.001187877,0.00008024498,0.00001152213,0.00001116335,0.000005900745,0.002489014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9102088,"threshold_uncertainty_score":0.9876614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05479192114434331,"score_gpt":0.353363800238195,"score_spread":0.2985718790938517,"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."}}