OS MODELOS DE PRÊMIO DA LEGISLAÇÃO ITALIANA COMO CAMINHO PARA PROCEDIMENTOS RECUPERACIONAIS MAIS EFETIVOS
Bibliographic record
Abstract
This article aims to study the premium sanctions in the new Italian business insolvency legislation, from the definition of the nation’s reorganization bankruptcy and its spirit to its potential applicability to the Brazilian bankruptcy system. Starting from the assumption that the aim of the receivership regime is not only the debt rehabilitation, but also preservation of the legal entity through a court-supervised reorganization, it is pondered which award model mechanisms (as known as premium sanctions) the Brazilian legislation failed to adopt when compared to the Italian law. In order to achieve this objective, both the articles and rules in the new Italian Code of the Company in Crisis and the amendments to Brazilian’s Federal Law No. 11.101/05, brought by Federal Law No. 14.112/2020, will be analyzed; as well as national and international doctrines applicable to the subject, with special emphasis on the similarity of the beginning and spirit of legislation. That said, we will seek to demonstrate how the provision of premium sanctions can positively influence the effectiveness of Brazilian legislation.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".