Bibliographic record
Abstract
As was noted in Chapter 4, some of the offenders who developed major mental disorders began offending in early adolescence and as was presented in Chapter 5, some of them had been identified even before that by the child welfare agency for substance abuse. Others who also developed a major mental disorder did not begin offending until adulthood. Among the offenders, the early-starters were defined as those who were convicted of their first offence before the age of eighteen, and the late-starters as those who were convicted of their first offence at or after the age of eighteen. PREVALENCE OF EARLY AND LATE-START OFFENDERS The prevalence of the early and late-start offenders is presented in Table 6.1. The increased prevalence of offenders among both the males and females with major mental disorders as compared to the non-disordered subjects is reflected in greater proportions of both early and late-starters. However, there is a significant gender difference, both among the disordered and the non-disordered. Among the males, the proportions of early-start offenders are greater than the proportions of late-start offenders. The reverse is true among the females. COMPARISONS OF OFFENDING OF THE EARLY AND LATE-START OFFENDERS Males Table 6.2 presents comparisons of the proportions of early and late-start male offenders convicted of non-violent and violent offences. Consider first the men with major mental disorders. Half of the early-starters as compared to 13% of the late-starters had been convicted of eleven or more non-violent offences.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".