Using Criminal Law to Fight Corruption: The Potential, Risks, and Limitations of Operation Car Wash (<i>Lava Jato</i>)
Bibliographic record
Abstract
Abstract The Brazilian case of Lava Jato started with a scandal involving the massive malfeasance of corporate and political elites in relation to the state-run oil company Petrobras. The scope of the corruption was unprecedented. Politicians and Petrobras employees received hundreds of millions (if not billions) of dollars in kickbacks between 2004 and 2012. This Article focuses on the innovations promoted by the Lava Jato case. This new jurisprudence has not only played a key role in breaking a long-lasting tradition of impunity in Brazil, but it has also generated much controversy. On the one hand, many Brazilian citizens welcomed the changes, as they allowed judges to overcome the obstacles faced by courts in previous corruption cases. On the other hand, opponents argue that the case is not solidly grounded in rule of law principles. Instead of taking sides in this debate, this Article tries to reframe it by arguing that there may be benefits associated with these novel interpretations, but there may also be costs and risks.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".