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Record W2971886303 · doi:10.31542/j.cb.1835

How Youth Are Defined

2019· article· en· W2971886303 on OpenAlexaffvenue
Alaina Brosseau

Bibliographic record

VenueCrossing Borders Student Reflections on Global Social Issues · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPunitive damagesRecidivismIntervention (counseling)CriminologyDisconnectionSet (abstract data type)Juvenile delinquencySocioeconomic statusPositive Youth DevelopmentPsychologyPolitical scienceSociologyComputer scienceDevelopmental psychologyLawPsychiatryDemography

Abstract

fetched live from OpenAlex

This project examines alternative measures to charging at-risk youth and the importance of transitional programs due to the ineffectiveness of punitive approaches. Punitive approaches are known for worsening issues with delinquent youth, such as recidivism. Youth are optimal to examine when tackling socioeconomic issues such as these because they are young enough that intervention can be done to set them on the right path. This intervention can prevent harms that would otherwise define them for the rest of their lives. There are shortcomings in the way society handles delinquent youth, and many are trapped in their criminal label. Because of this, they often continue to offend and ‘rebel’ against the system. Alternative measures to charging youth and transitional programs could make the difference in the way delinquent youth choose to move forward as adult members of society, potentially preventing criminal career formation.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.022
Scholarly communication0.0130.011
Open science0.0010.010
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0110.002

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 head0.082
GPT teacher head0.478
Teacher spread0.396 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2019
Admission routes2
Has abstractyes

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Same venueCrossing Borders Student Reflections on Global Social IssuesSame topicCrime Patterns and InterventionsFrench-language works237,207