Bibliographic record
Abstract
The purpose of this research project was to gain a better knowledge of juvenile sex offenders in the Montreal area. Do they present differences or similarities with other subjects from studies made in the United States? Can we improve our assessment techniques to better differentiate those who should be treated in a closed setting from those who could benefit from a follow-up in the community? And how does our juvenile justice system deal with this type of offender? In order to find answers to these issues, ten (10) in-depth interviews with personality measures were conducted with juveniles who admitted (or were convicted of) sexual abuse. Moreover, a study of fifty (50) files from the Social Services relating to the same kind of behavior was done in the Montreal area. Our results are similar to other studies made elsewhere : those who where convicted of rape or child molesting committed their first (official) offense at a mean age of 14.5 years and 60 % of their victims were females of an average age of 9. For 38 % of our sample, the sexual offense is part of an heterogenous criminal career. Our personnality measures failed to differentiate between child molesters and rapists. However, on the Jesness Inventory, our ten subjects had high scores on the SM (Social Maladjustment) scale, and on AI (Asocial Index). One other significant finding was that the personnel involved with assessment and treatment of juvenile sex offenders had to get a better knowledge of the dynamics involved with such offenses to record basic information (victim's age, the exact nature of the behavior and the type of violence involved) and recommend appropriate treatment.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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 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".