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Record W4395042927 · doi:10.1177/26338076241247856

Approaches for supporting youth dually involved in child protection and youth justice systems: An international policy analysis

2024· article· en· W4395042927 on OpenAlexaboutno aff
Rubini Ball, Susan Baidawi, Anthony J. Fitzgerald

Bibliographic record

VenueJournal of Criminology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsnot available
FundersAustralian Research Council
KeywordsEconomic JusticeChild protectionPolitical scienceCriminologyPsychologyLaw

Abstract

fetched live from OpenAlex

The high representation of children involved across both child protection and youth justice systems remains a pressing concern. Contributing factors include unnecessary police intervention for behavioural difficulties in residential care, and deficient systems integration particularly between child protection and youth justice. Policy reforms in the past 15–20 years have aimed to prevent and address this concern across jurisdictions such as Australia, New Zealand, Canada, the United Kingdom, and the United States of America. The study offers an updated review and analysis of these policies, targeting researchers, policymakers, and practitioners in the field. Examination of selected available policies identified four main strategies utilised: joint practice protocols, policies aimed at reducing the criminalisation of children in out-of-home care, crossover court lists, and specialised practice models like the Crossover Youth Practice Model (CYPM). There is promising evidence for some approaches, notably the CYPM, however, most suffer from a lack of implementation and outcomes evaluation, insufficient diversity considerations, and minimal inclusion of lived experience in design and implementation. Findings suggest future policy reforms should prioritise the development of whole-of-government strategies, involve children's perspectives, emphasise prevention, restorative and diversionary responses, multi-agency collaboration, ongoing support for implementation, and rigorous evaluation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.404
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

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

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 head0.176
GPT teacher head0.362
Teacher spread0.186 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2024
Admission routes1
Has abstractyes

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