As pesquisas sobre o “sentencing”: disparidade, punição e vocabulários de motivos
Bibliographic record
Abstract
O presente artigo propõe-se a traçar um panorama das pesquisas empíricas, estrangeiras e nacionais, so- bre o processo de determinação da pena pela justiça criminal, o “sentencing”. Apontamos a trajetória des- se campo, suas principais conclusões, bem como os pontos cegos de tais estudos. O objetivo é descrever os caminhos desse conjunto de pesquisas de modo a mostrar como os mesmos nos possibilitam refletir sobre a problemática do padrão de funcionamento desigual da justiça criminal apontado não somente por muitos estudos, mas também pelo imaginário so- cial sobre a justiça criminal. A partir do levantamento da literatura sobre o tema, bem como da análise dos principais trabalhos apontados, propomos uma re- qualificação do problema da disparidade das senten- ças criminais, bem como indicamos um novo objeto a ser explorado e, assim, para novas possibilidades de pesquisa empírica sobre o sentencing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".