Implementing independent regulatory agencies in Brazil: The contrasting experiences in the electricity and telecommunications sectors
Bibliographic record
Abstract
Abstract This paper explores hypotheses that could explain both the creation of independent regulatory agencies (IRAs) in Brazil, and the differences in the design of the Brazilian IRAs in the telecommunications and electricity sectors. To formulate specific hypotheses that make sense of the Brazilian case, the paper critically interrogates the “weak state” hypothesis and the “political bias” hypothesis. The first argues that countries with flawed governance structures, such as Latin American countries, are less likely to establish independent regulators than European countries. The second argues that “political bias” is a determinant factor in predicting the implementation of IRAs in Latin America. The first part of the paper uses these two general hypotheses as a basis to formulate specific hypotheses to explain the creation of IRAs in Brazil. The second part of the paper formulates specific hypotheses that could explain why institutional guarantees of IRA independence are stronger in the telecommunications sector, than in the electricity sector. In particular, the paper argues in support of a revised version of the “political bias” hypothesis to explain sectoral divergence, suggesting that bureaucratic resistance to reform may be the cause for the variations observed in Brazil between regulatory reform in electricity and in telecommunications.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".