RCP: Early Preventive Intervention for Disruptive Boys Can Improve Education and Reduce Later Criminality
Bibliographic record
Abstract
Early preventive intervention for boys at high risk of antisocial behaviour can improve their educational chances and reduce later criminality, a new study has found. Many intervention programmes have tried to reduce disruptive behaviour problems during early childhood to prevent school drop-out, violence and criminality during adolescence and adulthood. The aim of this Canadian study, published in the November 2007 issue of the British Journal of Psychiatry, was to assess the long-term impact and clinical significance of 2-year 'multicomponent ' preventive intervention on criminal behaviour and academic achievement. The Montreal Longitudinal Experimental Study is prospective, and has been examining the development of a large sample of boys attending inner-city kindergartens who have backgrounds of low socio-economic status, with a particular focus on antisocial behaviour and social adjustment. 250 disruptive-aggressive boys considered to be at risk of later criminality and low school achievement, identified from a community sample of 895 boys, were randomly allocated to an intervention or a control group. The rest of the sample (645) served as the low-risk
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 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".