{"id":"W4388240474","doi":"10.2139/ssrn.4621562","title":"Voter-Buying, Politician Selection, and Public Good Provision in Brazil","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Politics and Society in Latin America","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Selection (genetic algorithm); Business; Public spending; Voter model; Public economics; Economics; Public administration; Political science; Politics; Computer science; Mathematics; Law; Statistics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001612423,0.0001293635,0.0004986973,0.0008915841,0.0009588927,0.001482393,0.0003808382,0.0004857567,0.006437114],"category_scores_gemma":[0.007329072,0.0001873864,0.0003118594,0.001502679,0.001130667,0.0005174244,0.0008732295,0.0006088414,0.0002239543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003713521,"about_ca_system_score_gemma":0.002494175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1843416,"about_ca_topic_score_gemma":0.2625206,"domain_scores_codex":[0.9992237,0.000237021,0.00003055827,0.0000984434,0.00009590186,0.0003143337],"domain_scores_gemma":[0.9953985,0.002480119,0.001086129,0.0002272207,0.0003939663,0.0004142066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000415949,0.0003446165,0.8674577,0.000274286,0.0001612859,0.0008195429,0.009320829,0.002075329,0.0018467,0.08797048,0.001791954,0.02752128],"study_design_scores_gemma":[0.0001434642,0.0001508171,0.953746,0.000141737,0.0002242381,0.0002394988,0.01292711,0.006914047,0.0008838942,0.01299577,0.01160166,0.00003168161],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828653,0.0004054116,0.0001642389,0.001489554,0.000004468834,0.0000207882,0.0002159582,0.000008131931,0.01482621],"genre_scores_gemma":[0.9993254,0.00007481709,0.00002826802,0.00002320298,0.000002145408,0.000001892633,0.00002452476,0.000001400847,0.0005185061],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1843416,"threshold_uncertainty_score":0.3665373,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02167738211641225,"score_gpt":0.3360401883528297,"score_spread":0.3143628062364175,"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."}}