Corrupção e pobreza nos municípios brasileiros
Bibliographic record
Abstract
O artigo analisa, por meio de um modelo de dados em painel, o impacto da corrupção na pobreza dos municípios no Brasil entre 2005 e 2016, considerando, como indicador de pobreza, o Índice Firjan de Desenvolvimento Municipal (IFDM) e, como indicador de corrupção, o número de processos investigados e julgados, relacionados à tal prática, extraídos da Controladoria-Geral da União (CGU). Entre os resultados obtidos, constatou-se que quanto maior a ocorrência de práticas corruptas nos municípios, menores são as chances de uma elevação no IFDM e, por conseguinte, de diminuição dos níveis de pobreza. Diante das evidências apresentadas neste estudo, sugere-se a implantação de medidas rígidas de controle à corrupção a fim de evitar desperdício de recursos públicos direcionados à erradicação da pobreza.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".