Accountability, Corruption and Local Government: Mapping the Control Steps
Bibliographic record
Abstract
The central purpose of this paper is to map out the Brazilian web of accountability institutions and observe how institutions establish links with each other in order to control corruption cases that reach them. Focus is on institutions that are part of the Brazilian anti-corruption agenda, which include the Federal Public Prosecutor's Office, the Federal Police, the Office of the Comptroller General, the Federal Court of Accounts, the Federal Justice and the Ministries. In the literature, the most widespread argument is that, despite recent institutional improvements, the result produced by this web in terms of coordination is still weak. This article tests this claim by looking at the program called 'Inspections from Public Lotteries'. Through a longitudinal approach, I observed the flux of control activities among the institutions, especially the establishment of investigative and judicial proceedings. Not only I explored the extent to which corruption impacts the establishment of interactions, but I also investigated how the interactions affect the speed of judicial proceedings – using logistic regressions and survival analysis. The conclusion is that the Brazilian web is able to articulate itself in order to hold public officials accountable (something new in this recent democracy), but not in a homogeneous way across all institutions (something the literature has missed). Furthermore, I demonstrate that the entire web of accountability institutions is unable to arrive at a decision in a timely manner.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".