Drug sentencing in Brazil: factors influencing sentencing outcomes in the criminal courts of Recife/PE
Bibliographic record
Abstract
Purpose This paper aims to examine sentencing decisions for drug-trafficking offences in the criminal courts of the city of Recife to address a gap in quantitative research on drug sentencing and incarceration in Brazil. Design/methodology/approach Using original data obtained from the Court of Justice for Pernambuco, the research used multivariate regression analysis to investigate the effect of case processing, offender, and offence characteristics on sentence length. Findings A key finding of the research is the influence of two legal factors on sentence length: admitting to a drug-trafficking offence and being categorized as “mitigated trafficking”. Results also indicate that first-time defendants were more likely to be categorized as mitigated trafficking, stressing the importance of criminal history on predicting sentencing outcomes. “Mitigated trafficking” is a distinct category of drug-trafficking created by the Drug Law nº. 11.343 (2006) to protect defendants considered novices in the illicit drug market from receiving longer imprisonment sentences. Practical implications The findings suggest that the policy strategy of having a legal distinction for a specific type of defendant appears to be effective in impacting sentence length for drug-trafficking convictions. Future research could explore how similar strategies could be adopted to influence sentencing for other vulnerable groups. However, focussing on a defendant records or prior convictions as an eligibility criterion could disproportionately impact defendants who are caught in a cycle of re-offending for socio-economic reasons or a need to finance a substance use disorder. Originality/value This research address a gap in quantitative sentencing research in Brazil and contributes to the broader literature by presenting results that are aligned with previous studies conducted in North America.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".