The Brazilian Clean Company Act: Using Institutional Multiplicity for Effective Punishment
Bibliographic record
Abstract
In Brazil’s battle against corruption over the past two decades, there has been significant progress associated with the systems of oversight and investigation but very little progress in holding corrupt actors legally accountable for their transgressions. We suggest that until very recently this could be partially explained by the fact that there was institutional multiplicity (i.e., duplication of functions) in oversight and investigative institutions, while at the punishment stage, a single and underperforming institution—the judiciary—exercised monopolistic authority. To circumvent the limits associated with Brazilian courts, the government is increasingly relying on administrative sanctions for corruption. It is in this context that Brazil has enacted legislation to punish legal persons for both foreign and domestic corruption: The Clean Company Act (Lei Anti-Corrupção), enacted in August 2013, has used institutional multiplicity in an attempt to circumvent the well-known problems that plague the Brazilian anti-corruption system. We suggest that this approach looks promising, as it follows the same structure of recent reforms that have been successful in Brazil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| 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".