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Record W4394904912 · doi:10.21428/cb6ab371.bcdf1219

Early Developmental Crime Prevention Forged through Knowledge Translation: A Window into a Century of Prevention Experiments

2024· preprint· en· W4394904912 on OpenAlexaboutno aff
Brandon C. Welsh, Richard E. Tremblay

Bibliographic record

VenueCrimRxiv · 2024
Typepreprint
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsCrime preventionWindow (computing)Translation (biology)CriminologyPsychologyHistoryComputer scienceBiologyWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.124
GPT teacher head0.407
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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