Juvenile Sex Offending Through a Developmental Life Course Criminology Perspective
Bibliographic record
Abstract
Current American policies and responses to juvenile sex offending have been criticized for being based on myths, misconceptions, and unsubstantiated claims. In spite of the criticism, no organizing framework has been proposed to guide policy development with respect to the prevention of juvenile sex offending. This article proposes a developmental life course (DLC) criminology perspective to investigate the origins, development, and termination of sex offending among youth. It also provides a review of the current state of knowledge regarding various parameters characterizing the development of sex offending (e.g., prevalence, age of onset, frequency, persistence, continuity in adulthood, and versatility). The review highlights some heterogeneity across these developmental parameters suggesting the presence of different sex offending patterns among youth. In fact, it is proposed that, based on the current knowledge, such heterogeneity can be accounted for by a dual taxonomy of adolescents involved in sexual offenses: (a) the adolescent-limited and (b) the high-rate/slow-desister. The DLC criminology approach and the dual taxonomy are proposed as organizing frameworks to conduct prospective longitudinal research to better understand the origins and development of sex offending and to guide policy development and responses to at-risk youth and those who have committed sexual offenses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".