Dark Side Case: Layers of Corruption in the Car Wash Scheme
Bibliographic record
Abstract
Petrobras is an oil and gas giant publicly traded in Brazil and in international stock markets such as the New York Stock Exchange. The company’s main shareholder is Brazil’s federal government. Political influence is at the center of Petrobras’ implication in a massive corruption scheme revealed in 2014. Brazilian Federal Police’s Operation Car Wash disclosed a wide corruption scheme involving Petrobras, members of the Brazilian public administration, and some of the top construction firms in the country. The case focuses on describing how corruption practices spread at Petrobras as well as on the company’s responses to the crisis initiated in March 2014. In the months that followed the scandal, former Petrobras executives involved in the scheme agreed to plea bargains and thus helped reveal an intricate network of corruption practices, including complex forms of bid rigging and bribery. Meanwhile, Petrobras announced the intention to create a Governance, Risk, and Compliance unit that would be in charge of various compliance initiatives. On the other hand, the company expressly denied liability and portrayed itself as a victim of the scheme. Based on rich information from the Car Wash scandal, the case addresses the following questions: what is organizational corruption and what are the main assumptions underlying the design and implementation of anti-corruption strategies?
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".