Calidad regulatoria en Perú: de la simplificación administrativa al análisis de impacto regulatorio en cámara lenta
Bibliographic record
Abstract
This paper reviews and discusses a wrong distinction that has arisen in Peru —at both legal and conceptual levels— between (i) the so-called regulatory quality analysis, even though it is only an additional administrative simplification tool, and (ii) the improvement of regulatory quality through several tools including regulatory impact assessment —RIA. With this aim, it describes the concepts and initiatives implemented by the best international practices concerning, on one hand RIA as a cornerstone to ensure the quality of substantive regulations —economic and social regulations— and on the other hand, administrative simplification tools to reduce red tape as complementary measures. Likewise, it explains the developments in administrative simplification in Peru, including the launching of the so-called regulatory quality analysis program 5 years ago, reducing it only to a —more sophisticated administrative simplification tool. This paper describes also the scattered and ineffective initiatives on RIA that have been carried out in Peru, as well as the latest RIA regulation which is still a fragile effort to effectively implement an integral regulatory reform. In sum, Peruvian regulatory reform has prioritized ancillary mechanisms rather than substantive ones. The mandate to enforce RIA is weak because it has been only passed by a regulation rather than a law, and the approval of relevant provisions to make RIA effective may still be delayed. Therefore, regulatory reform is yet incomplete, vulnerable, and focused on administrative simplification.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".