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Record W2040643124 · doi:10.1080/14999013.2014.885470

The Use of the SAVRY and YLS/CMI in Adolescent Court Proceedings: A Case Law Review

2014· article· en· W2040643124 on OpenAlexaffabout
Taryn A. Urquhart, Jodi L. Viljoen

Bibliographic record

VenueInternational Journal of Forensic Mental Health · 2014
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRisk assessmentPsychologyLegal riskRisk management toolsLegal serviceRisk managementCriminologyCourt decisionAdolescent medicineService (business)Case managementLawPolitical sciencePsychiatryComputer securityBusinessComputer science

Abstract

fetched live from OpenAlex

Despite the continued growth of adolescent risk assessment tools, we do not know how these tools are being used in adolescent court cases or how this information influences legal decision making. To address this gap, we reviewed 50 Canadian, American, and international adolescent offender cases using the Structured Assessment of Violence Risk in Youth or Youth Level of Service/Case Management Inventory. The results confirm that adolescent risk assessment tools are primarily introduced during sentencing or adult transfer proceedings. Judges identified the specific risk and protective factors of youth in 36.2% and 19.0% of cases, respectively. In terms of legal decision making, the risk assessment was either directly or indirectly referred to in 76.0% of cases; however, judges most often placed some weight on the risk assessment as a part of an enumerated list of other important factors. Although risk assessments were generally considered admissible in these cases, some legal concerns were raised, particularly with the use of risk assessments to guide sentencing decisions.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.397

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)0.0000.000

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 head0.060
GPT teacher head0.370
Teacher spread0.310 · 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 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

Citations11
Published2014
Admission routes2
Has abstractyes

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