The linkage between childhood bullying behaviour and future offending
Bibliographic record
Abstract
AIM: To examine the linkage between bullying behaviour in early childhood and any subsequent contact with the criminal justice system. METHODS: A Canadian sample (570 boys and 379 girls) was derived from clients who participated in the evidenced-based programme, SNAP (STOP NOW AND PLAN), between 2001 and 2009. A court order was obtained to access any criminal record data on participants. The Early Assessment Risk Lists (EARL-20B and EARL-21G) and the Child Behavior Checklist (CBCL) were used to identify level of risk and bullying behaviour. Outcome variables included age the child first came in contact with the criminal justice system and frequency. RESULTS: Logistic and Cox regression analyses indicate that the risk of onset of criminal offence for bullies was significantly higher than for non-bullies. The hazard of criminal offence for bullies is 1.9 times (95% CI: 1.1-3.2) than that of non-bullies. This holds true even when adjusted for age, gender and other risk factors. CONCLUSION: We found a strong linkage between bullying behaviour during childhood and subsequent criminal offending after the age of 12. Criminal convictions for bullies were nearly twice as high for non-bullies up to the child's 18th birthday. EARLs were effective in differentiating risk associated with bullying.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".