Coordinating the Enforcement of Anti-Corruption Law: South American Experiences
Bibliographic record
Abstract
One of the most pressing challenges in anti-corruption law is whether and how to coordinate enforcement across multiple agencies, that is to say, under conditions of institutional multiplicity. One approach is modular enforcement, which involves dividing responsibility for enforcement among multiple institutions that are able, but not required, to coordinate their activities. The relatively impressive performance of Brazil’s anti-corruption agencies around the beginning of the twentieth century has been attributed to this kind of institutional modularity. We examine whether other similarly situated countries adopted the Brazilian approach. Specifically, we compare the extent to which the modular approach to anti-corruption enforcement was reflected in the national anti-corruption institutions of Brazil and five other South American countries as of 2014. We find little evidence that Brazil’s neighbors adopted the modular approach and suggest a variety of political, intellectual and institutional factors that may limit the attraction of institutional modularity outside the Brazilian context. Our analysis also demonstrates the value of an approach to comparative legal analysis which extends beyond the judiciary and the police to cover the full range of institutions involved in law enforcement.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".