{"id":"W3126003125","doi":"","title":"INSTITUTIONS, INEQUALITY, AND LONG-TERM DEVELOPMENT: A PERSPECTIVE FROM BRAZILIAN REGIONS","year":2016,"lang":"en","type":"preprint","venue":"Americanae (AECID Library)","topic":"Income, Poverty, and Inequality","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inequality; Economic inequality; Context (archaeology); Industrialisation; Geography; Development economics; Urbanization; Census; Social inequality; Demographic economics; Economic growth; Economics; Demography; Sociology; Population","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":[0.001770029,0.0003040122,0.0005584541,0.003537533,0.001661181,0.002713415,0.0005821161,0.0005043977,0.001592195],"category_scores_gemma":[0.005920892,0.0001831178,0.0005563611,0.007915248,0.002206295,0.00217614,0.002403003,0.000783148,0.00007609812],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002119001,"about_ca_system_score_gemma":0.001468683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1690367,"about_ca_topic_score_gemma":0.214906,"domain_scores_codex":[0.9992094,0.0003169054,0.00005311388,0.0001124972,0.0001176401,0.0001903783],"domain_scores_gemma":[0.9936068,0.002896346,0.001895818,0.0003983207,0.0007271181,0.0004755437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008506232,0.00009576088,0.7782835,0.0004647459,0.0002286874,0.0009122979,0.0375638,0.002141925,0.0008840599,0.122913,0.001311454,0.05511574],"study_design_scores_gemma":[0.00001052298,0.00006668633,0.9212111,0.000951356,0.0001748628,0.000439209,0.03182504,0.002123807,0.0002890963,0.02127043,0.02158744,0.0000504142],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9226182,0.02499139,0.002233443,0.006263637,0.00003469261,0.00001645712,0.001063,0.00001265636,0.04276653],"genre_scores_gemma":[0.9954633,0.003550496,0.000351478,0.000146079,0.00002088412,0.000008462969,0.000217133,0.00000399675,0.0002382055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1690367,"threshold_uncertainty_score":0.3361056,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04927918190305072,"score_gpt":0.3305477495810028,"score_spread":0.2812685676779521,"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."}}