Patterns of Residual Criminal Careers among a Sample of Adjudicated French-Canadian Males
Bibliographic record
Abstract
This study investigates distributions of residual career length (RCL), which refers to the number of years remaining in criminal careers up to the point of termination. Analyses are based on a sample of French-Canadian adjudicated males from the Montreal Two Samples Longitudinal Study and include both self-reports and official records up to age 40. Distributions of RCL are presented according to age, serial conviction number, time since the previous conviction, age of onset, and offence type. Findings show that the number of years remaining in criminal careers declines at a steady pace with age, even among this sample of serious and persistent offenders. RCL also tends to decline with each successive conviction, with increased time lags between the current and previous offences, and with later onset. Furthermore, the accuracy of predictions of RCL based on information available in official records is notable, given the scepticism of past research on this issue. Distributions of self-reported and official RCL were often highly similar, suggesting that official records appear to provide a reasonably accurate picture of criminal careers among high-rate offenders. Theoretical and policy implications are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".