Economic Concentration of Public Electoral Financing in Brazil
Bibliographic record
Abstract
The political dynamics in Brazil highlight the growing competitiveness of elections, requiring significant financial resources. The prohibition of corporate donations in 2015, prompted by Operation Car Wash, increased the importance of public funds in campaigns. In 2022, the Special Campaign Financing Fund (FEFC), or “electoral fund,” reached R$4.9 billion. Along with the Party Fund, public financing accounted for 81% of candidates’ revenues, the highest share of public money in the country’s history. This study aims to understand the distribution of these resources among parties for candidates running for the Chamber of Deputies in 2022, using a quantitative approach with the Gini coefficient as an indicator. The results were used to create a ranking of public resource distribution by party. The national index was 0.72, indicating a high concentration in resource allocation. This result was compared with the ideological classification of parties, showing no strong correlation with party positions. It was also analyzed regionally, observing the distribution across states.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".