Surviving Corruption in Brazil: Lula's and Dilma's Success despite Corruption Allegations, and Its Consequences
Bibliographic record
Abstract
This article analyzes the continued popular support for Lula and Dilma in the face of multiple corruption allegations throughout their respective presidencies. What explains their ability to survive corruption? And what are the implications of this – at first sight – lack of electoral punishment for Brazilian democracy? In searching for answers to these questions, this article looks at four mechanisms that help explain the continued popularity of politicians amid allegations of corruption: the use of clientelism as payoffs, informational failures, the relevance of other issues, and rouba mas faz. By analyzing Lula's and Dilma's terms in office and their inopportune links to corruption, this article argues that the shifting strategies used to deal with corruption allegations effectively shifted the reputational costs of corruption away from individual political leaders and toward the Workers’ Party and the political system as a whole. This finding emphasizes the mid- to long-term consequences of corruption scandals on political parties and democratic institutions, while also shedding light on the paradoxical relationship between corruption as a voting valence issue and continuing electoral support for politicians allegedly involved in corruption.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".