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Record W3209610649 · doi:10.11575/prism/32675

Best Practices for Chronic/Persistent Youth Offenders

2009· article· en· W3209610649 on OpenAlexfundaboutno aff
B. DeGusti, L. MacRae, Manuel Vallée, Tullio Caputo, Joseph P. Hornick

Bibliographic record

VenueOpen MIND · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersUniversity of WaterlooQueen's University
KeywordsCriminologyPsychologyChronic povertyPolitical sciencePovertyLaw

Abstract

fetched live from OpenAlex

In 2006, the Canadian Research Institute for Law and the Family (CRILF) began work on the three-year project, A Study of Youth Offending, Serious Habitual Offenders, and System Response in Calgary. One objective of this study was to develop an understanding of the predictors of chronic and persistent youth offending, as well as a knowledge base of best practices in Canada and internationally for this population of youth offenders. With funding from the Alberta Law Foundation and the National Crime Prevention Centre, and in partnership with the Centre for Initiatives on Children, Youth and the Community, City of Calgary Community and Neighbourhood Services, and Calgary Police Service, CRILF researchers examined the literature on chronic and persistent youth offenders, and performed an environmental scan to assess what programs and strategies police agencies across Canada have in place to address this youth offending population. The objectives of this report are to: 1) understand the predictors of chronic and persistent youth offending; and 2) examine best practices used by police agencies across Canada for chronic and persistent youth offenders.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.001

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.

Opus teacher head0.188
GPT teacher head0.396
Teacher spread0.209 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations1
Published2009
Admission routes2
Has abstractyes

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