Early Developmental Crime Prevention Forged through Knowledge Translation: A Window into a Century of Prevention Experiments
Bibliographic record
Abstract
Purpose To begin to develop an understanding of knowledge translation of early developmental crime prevention.Methods Involves a narrative review of experiments of early developmental prevention with measures of delinquency and criminal offending, and profiles two leading experiments, the Cambridge-Somerville Youth Study (CSYS) and the Montréal Longitudinal-Experimental Study.Results While the roots of early developmental crime prevention can be traced to studies of human development, experiments of preventive interventions are at the heart of knowledge translation and policy influence. This can be seen in the form of replications, the process of scaling up effective interventions for wider dissemination, and inspiration for prevention scientists to launch new and innovative experiments—sometimes with the aim to improve upon past results. For example, far from curtailing policy interest in a developmental approach to delinquency prevention or dampening the need for prevention experiments, the harmful effects reported in the 30-year follow-up of the CSYS instead had an influence on some new longitudinal-experimental studies in developmental and life-course criminology.Conclusions New experiments are needed to continue to advance early developmental crime prevention, and further research is needed to add to our understanding of knowledge translation in this area.
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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 | 0.170 | 0.194 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.050 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".