Some Temporarily Successful Experiences along Three Decades of Failure: Crime and Public Policy in Brazil
Bibliographic record
Abstract
Many adolescents manifest delinquent behavior, but only a few are responsible for most of the offenses and the serious crimes. To know the differences in the criminal engagement and in the personal variables related to the more persistent/severe antisocial behavior is important to adjust the Juvenile Justice Systems to the adolescents needs. In the Brazilian Justice System, this is not considered. Although the law indicates the importance of personalizing legal and social responses to each juvenile offender, the treatment is essentially undifferentiated. Therefore, the purpose of this study was to identify and describe personal variables that discriminate subgroups of Brazilian adolescents with different levels of criminal engagement. A total of 193 male adolescents (133 recruited in schools and 60 institutionalized) answered a Self-Report Delinquency Questionnaire, which included scales of psychosocial constructs. Five groups were found by the Ward and K-means clustering methods. The adolescents were compared on variables such as personal traits. Those groups with major criminal engagement had higher levels of impulsivity (η² = 0.08; p = 0.002), higher antisocial values (η² = 0.08; p = 0.003), and higher prevalence of alcohol (X² = 103.75; p < 0.001) and marijuana use (X² =257.61; p < 0.001). This finding confirms the specialized literature, denoting how important it is to identify and understand the differences in the criminal engagement of adolescents.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".