{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000358042,0.0004654036,0.0007080763,0.0002566706,0.001030766,0.0002197635,0.0009899287,0.0004027606,0.001067516],"category_scores_gemma":[0.0003874305,0.0004193118,0.000192089,0.0004376155,0.001915723,0.001170822,0.001338877,0.0006016713,0.0001114513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004980535,"about_ca_system_score_gemma":0.003276418,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1592987,"about_ca_topic_score_gemma":0.007104715,"domain_scores_codex":[0.996282,0.000739375,0.0006303657,0.001071922,0.0006234471,0.0006528356],"domain_scores_gemma":[0.9977276,0.0003355806,0.0005188984,0.000781928,0.0001367044,0.0004993045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004215858,0.0001676763,0.7207668,0.00004197532,0.0002749618,0.00004436651,0.0230905,4.685651e-7,0.000006942499,0.2352076,0.009956297,0.01040022],"study_design_scores_gemma":[0.0004101709,0.00003924197,0.9009033,0.0003403362,0.00006430221,9.546948e-7,0.003734673,0.000001585105,0.00005858377,0.05595787,0.03754804,0.0009409317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8359272,0.002342687,0.004843325,0.02455599,0.001835523,0.001073224,0.0007175747,0.0007201678,0.1279843],"genre_scores_gemma":[0.9887785,0.001937143,0.001976811,0.002455852,0.001185546,0.0001027996,0.0002551134,0.00005566044,0.003252563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1801365,"threshold_uncertainty_score":0.9998456,"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."}}