Bibliographic record
Abstract
The use of group‐based trajectory analysis has yielded important insights into the nature and pattern of offending over the life span. Particularly important is research on the progression of criminal activity across major developmental periods such as adolescence to adulthood and early adulthood to mid‐adulthood and beyond. The purpose of this study was to: (1) compare the criminal trajectories of our sample of 378 juvenile offenders generated for a M = 12.1‐year follow‐up period, from age 15 to 27 years (on average), with the criminal trajectories generated for a M = 18.7 year follow‐up period, from age 15 to 34 years (on average); and (2) apply cross‐ validation (CV) to determine the optimal number of groups as an alternative to the Bayesian Information Criterion (BIC), which is known to be problematic. The trajectory analyses found that a 5‐group model best fit the initial follow‐up data and an 8‐group model best fit the extended follow‐up data. Trajectory groups with shorter trajectory lengths at the 12.1 year follow‐up were more stable over the two follow‐up periods than groups with longer trajectory lengths. Last, CV performed better than the BIC in finding the optimal models. 3
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.638 | 0.295 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".