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Record W2884411099 · doi:10.1177/0093854818785404

The Relationship Between Risk, Criminogenic Need, and Recidivism for Indigenous Justice-Involved Youth

2018· article· en· W2884411099 on OpenAlexafffund
Ilana Lockwood, Michele Peterson‐Badali, Fred Schmidt

Bibliographic record

VenueCriminal Justice and Behavior · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of TorontoLakehead UniversityThunder Bay Regional Health Sciences Centre
FundersUniversity of TorontoU.S. Department of Justice
KeywordsRecidivismIndigenousReceiptPsychologyEconomic JusticeCriminal justicePoison controlRisk assessmentIntervention (counseling)Environmental healthApplied psychologyCriminologyPsychiatryMedicineBusinessComputer securityPolitical science

Abstract

fetched live from OpenAlex

The risk–need–responsivity framework is widely used to guide the case management of justice-involved youth, but little research is available on its applicability to Indigenous populations. In the present study, we examined how standardized risk assessment, identification of criminogenic needs, and receipt of need-targeted programming related to recidivism in a sample of 70 Indigenous and non-Indigenous youth. The two groups did not differ on overall level of risk, number of needs, match to services, or recidivism rates. However, Indigenous youth were evaluated as higher risk in peer and leisure functioning, more likely to have needs related to education and leisure, and less likely to receive adequate peer-specific intervention. In both groups, risk assessment predicted recidivism, while match to services predicted days to reoffense. High rates of mental health issues and associated services were observed in both groups. Implications of these findings for research and practice with Indigenous youth are discussed.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.001
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 head0.128
GPT teacher head0.370
Teacher spread0.242 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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