Systemic corruption and institutional multiplicity: Brazilian examples of a complex relationship
Bibliographic record
Abstract
Systemic corruption is usually described as a stable self-reinforcing equilibrium that traps individuals by reducing incentives to behave honestly. This article assumes that law enforcement institutions may also be trapped in this equilibrium, leaving no alternative to individuals who want to report corruption. Would the existence of multiple institutions performing accountability functions – what we call institutional multiplicity – reduce the probability that all institutions would be trapped in a systemic corruption environment? We start by hypothesizing that even in contexts of systemic corruption there may be ‘pockets of honesty.’ If this is the case, institutional multiplicity, by increasing the number of accountability institutions available, may create avenues for individuals to report corruption. On the other hand, multiplicity may also increase the risk of ‘façade enforcement’ – that is, the mere appearance of accountability that reinforces a systemic corruption equilibrium. We illustrate these two scenarios with Brazilian examples. We end the article with a discussion of the design of accountability systems in contexts of systemic corruption, arguing that there may be advantages in preserving institutional multiplicity if its deleterious effects are addressed. While based on the Brazilian experience, this article advances theoretical hypotheses that may be useful to other countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".