{"id":"W2011713889","doi":"10.1108/17538251311329586","title":"Globalization and inequality: insights from municipal level data in Brazil","year":2013,"lang":"en","type":"article","venue":"Indian Growth and Development Review","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Economics; Globalization; Inequality; Industrialisation; Economic inequality; Latin Americans; Development economics; Per capita; Kuznets curve; Per capita income; Economic growth; Population; Political science; Sociology","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":[0.0007815381,0.0001431508,0.0003198945,0.00006245874,0.0002731867,0.0001220176,0.0003300923,0.00009981344,0.0001532582],"category_scores_gemma":[0.0005206548,0.0001215734,0.00001256224,0.0002946785,0.0001188034,0.0007392227,0.0002392412,0.0000993072,0.00005512994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005838538,"about_ca_system_score_gemma":0.0002782201,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02448839,"about_ca_topic_score_gemma":0.0150719,"domain_scores_codex":[0.9982436,0.0003521231,0.0004875117,0.0003495869,0.0003085725,0.0002585521],"domain_scores_gemma":[0.999252,0.0001063944,0.0001221796,0.0002717331,0.00005815919,0.0001895901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000008710867,0.0001536218,0.6740763,0.002450672,0.00005775144,0.00001839881,0.05373289,2.757069e-8,0.000009362891,0.02510152,0.005959119,0.2384317],"study_design_scores_gemma":[0.0005277089,0.00001373549,0.7045296,0.001897158,0.00001675827,0.000001225131,0.001438754,0.000009648482,0.00001349282,0.007063167,0.284015,0.000473672],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8733749,0.1048076,0.0002216037,0.004006705,0.0003228664,0.001531516,0.00009216514,0.00007151799,0.01557116],"genre_scores_gemma":[0.9084316,0.08334103,0.001524451,0.006111294,0.00009521256,0.00003608475,0.00038187,0.000008711017,0.00006971371],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2780559,"threshold_uncertainty_score":0.9820076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08946158799832522,"score_gpt":0.3408954073888204,"score_spread":0.2514338193904952,"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."}}