Childhood Predictors of Adult Criminality: A Meta-Analysis Drawn from the Prospective Longitudinal Literature
Bibliographic record
Abstract
Sufficient research now exists in the psychology of criminal conduct literature to address the long-term impact of early childhood and adolescent experiences on later adult outcomes. In the present meta-analysis, selected studies were prospective and longitudinal, tracking a variety of early childhood and family factors that could potentially predict later involvement in the adult criminal justice system. Thirty-eight studies met the selection criteria. Major findings indicate that dynamic versus static predictors are related to later adult criminal justice involvement. The older the child was at the time the predictor was measured, the stronger was the relationship to adult offending. Within the set of dynamic predictors, childhood and adolescent factors that rate most highly include a variety of behavioural concerns including early identification of aggression, attentional problems, motor restlessness, and attention seeking. Emotional concerns consistent with depression including withdrawal, anxiety, self-deprecation, and social alienation are also represented. Predictors also included family descriptors such as a variety of negative parenting strategies including coerciveness, authoritarian behaviours, lack of child supervision, and family structure variables such as witnessing violence, inter-parental conflict, family stressors, and poor communication. Results are discussed in relation to prevention strategies for targeted services that influence the probability of antisocial outcomes for children into adulthood.
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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.024 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".