Criminal Careers of Juvenile Sex and Nonsex Offenders
Bibliographic record
Abstract
Developmental criminologists have criticized typologies of juvenile sex offenders (JSOs) for assuming that JSOs involved in nonsexual offending are a homogenous group. However, this criticism has remained largely conceptual. To help empirically address the validity of this criticism, offending trajectories from age 12 to 23 were measured for a sample of male JSOs ( n = 52) and juvenile nonsex offenders (JNSOs; n = 231) interviewed as part of the Incarcerated Serious and Violent Young Offender study. Within this predominantly Caucasian sample, whether offender status (JSO/JNSO) or risk factors were better indicators of trajectory group membership was examined. Four unique offending trajectories emerged, namely, a low-rate offending trajectory, a bell-shaped offending trajectory, a slow-rising chronic trajectory, and a high-rate chronic trajectory. The relatively equal distribution of JSOs in each trajectory indicated that the criminal behavior committed by this group was not expressed by just one pattern. Further, the prevalence of JSOs in each trajectory mirrored the prevalence of JNSOs in the same trajectory, suggesting that having a sex offense in adolescence was not informative of general offending patterns. Individual and familial-level risk/needs factors were better indicators of trajectory membership. Implications for existing typologies of JSOs are discussed.
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 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".