1 Comparison of Adult Offense Prediction Methods Comparison of Adult Offense Prediction Methods based on Juvenile Offense Trajectories using Cross-Validation
Bibliographic record
Abstract
Considerable research has found support for the relationship between criminal offending in adolescence and criminal offending in adulthood. Estimating the strength and nature of the relationship has been facilitated by the methodological advances that have been made over the past decade. We add to this literature and describe and apply various prediction methods to examine the extent to which adult (ages 18–33 years) criminal offense trajectories can be predicted by juvenile (ages 9–17 years) offense trajectories. These methods include conventional models based on latent Poisson classes (LPC) and generalised linear models (GLM) and more sophisticated Cox proportional hazards models that predict entire adult-offense timelines. We also present a novel method, based on the exponential distribution, for adjusting the observed offense patterns for time-at-risk using secure custody information and a method for addressing the problem of the offense-conviction date lag. In addition, we discuss how to compare the accuracy of different prediction methods using cross-validation, thus providing a clear, unambiguous measure of prediction accuracy. We apply our methods to a data set comprising 378 male offenders in Toronto, Canada, whose criminal careers were
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 | 0.078 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".