Entre a lei e o juiz: Os processos decisórios na definição de penas
Bibliographic record
Abstract
EnglishThis paper presents an analytical framework to observe theoretical and empirical aspects of sentencing. More specifically this paper addresses the cases in which the judge considers mandatory minimum sentences to high and therefore inadequate to a particular offence and offender. To develop this point, the paper discusses Superior Court decisions from Brazil and Canada. portuguesO objetivo deste texto e contribuir para a construcao de um quadro analitico para observar os processos decisorios em materia de penas. Mais especificamente, o texto busca oferecer subsidios para a reflexao sobre as situacoes nas quais o(a) juiz(juiza), diante das situacoes concretas do caso, considera muito elevada, e, portanto, injusta, a pena definida abstratamente pelo legislador. Para avancar sobre esta questao, o texto discute: (i) a Sumula 231 do STJ – que impede a reducao da pena aquem do minimo legal pela incidencia de atenuantes – e sua confirmacao pelo STF e (ii) uma decisao judicial canadense que permite observar a possibilidade de construir decisoes sobre a pena com base na lei, na constituicao e na jurisprudencia, sem ter como ponto de partida a pena minima prevista no tipo penal
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".