“Democratizing punishment”? Federal prosecutors’ new anticorruption project and the Brazilian penal field
Bibliographic record
Abstract
Since the 2000s, Brazilian federal prosecutors increasingly worked on corruption and white-collar crime (WCC) cases. While scholars have focused on how this engagement impacted politics, few works examine its implications for the country’s penal field. In the context of the mass incarceration of working-class and racialized groups, does prosecutors’ focus on the powerful promote penal change or continuity? Using this issue’s concepts of professional trajectories, professional projects, and contexts, I investigate how prosecutors built their expertise on criminal law, how this process shaped their penal discourses and practices, and how these contrast with the country’s penal context. Empirically, I analyze prosecutors’ CVs and discourses on an anti-corruption bill. I find that prosecutors invested heavily in academic specialization and international training in criminal law, focusing on WCC. With this move, they created a new professional project, which breaks with traditional racialized tough-on-crime discourses and proposes measures to increase accountability for the powerful, seeking to “democratize punishment.” However, some of these discourses and practices reproduce traditional punitive approaches embedded in the Brazilian penal context, such as penal populism and the disavowal of procedural rights. Although prosecutors built their professional trajectories around corruption and WCC, their professional project may incidentally harm marginalized defendants.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.016 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".