Institutional Multiplicity and the Fight Against Corruption: A Research Agenda for the Brazilian Accountability Network
Bibliographic record
Abstract
Abstract Since democratization, Brazil has established a robust network of accountability institutions that perform a myriad of functions in combating corruption. While there is empirical research on the inner workings of the Brazilian accountability network, many questions remain unanswered and many dimensions of the interactions between institutions in this system have yet to be analyzed and uncovered. This paper argues that the accountability literature can benefit from further descriptive empirical studies, especially detailed analyses that account for empirical differences in norms, procedures and sanctions. To promote this type of granular empirical analysis, we formulate a research agenda for the Brazilian accountability system, arguing that the concept of institutional multiplicity has much to offer in this endeavour. Considering the difficulty in capturing the complexity of Brazil's vast network of accountability institutions in a single paper, we focus on the accountability systems for civil servants working for the federal executive branch. Despite being focused on a particular dimension of the accountability system, we hope that the topics proposed here can inform research questions about other areas of the system as well.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".