Using Institutional Multiplicity to Address Corruption as a Collective Action Problem: Lessons from the Brazilian Case
Bibliographic record
Abstract
The academic literature has traditionally framed corruption as a principal-agent problem, but recently scholars have suggested that the phenomenon may be more accurately described as a collective action problem, especially in cases of systemic and widespread corruption. While framing corruption as a collective action problem has proven useful from a descriptive point of view, it has not offered many helpful suggestions for policy reforms. This paper tries to address this gap by suggesting that “institutional multiplicity” (a concept used other areas of research but not in the corruption literature) could be a feasible reform strategy to deal with corruption as a collective action problem. The paper distinguishes between proactive and reactive institutional multiplicity, and argues that the latter's creation of separate institutions could potentially reduce the costs for those who are inclined to engage in principled behavior to deviate from the standard corrupt behavior that prevails in society. This allows for incremental, but potentially very transformative change. Also, institutional multiplicity allows for the creation of new institutions without dismantling the existing ones. It is therefore less likely to face political resistance from interests who benefit from the status quo. We provide some anecdotal evidence to support this claim by analyzing Brazil's recent surge of anti-corruption efforts which could be, at least in part, attributable to the existence of institutional multiplicity in the country's accountability system. In addition to offering a hypothesis to interpret recent experiences with combating corruption in Brazil, the paper also has broader implications: if the hypothesis proves correct, institutional multiplicity could help reformers in other countries where corruption is systemic.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 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".