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Record W2582711641

Variations In Juvenile Offending in Louisiana: Demographic, Behavioral, Geographic, and School-Related Predictors

2016· article· en· W2582711641 on OpenAlexfundno aff
Samuel B. Robison, Bret J. Blackmon, Judith L. F. Rhodes

Bibliographic record

VenueAquila Digital Community (University of Southern Mississippi) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Center for Advancing Translational SciencesNational Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchOffice of Justice ProgramsUniversity of WashingtonNational Institutes of HealthU.S. Department of Justice
KeywordsJuvenile delinquencyPovertyPopulationCriminologyCensusDemographyPsychologySocioeconomicsGeographySociologyPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

This study examines the relative impacts of demographic, behavioral, and school-related factors on juvenile justice contact of varying magnitudes (felony, misdemeanor, and status offenses) across a large, and non-selective sample of youths. The sample includes Deep South public school students examined from 1996 to 2012 (N =615,515). Data were obtained through state administrative databases. Noteworthy findings are that school expulsion, male gender, prior Louisiana Office of Juvenile Justice (LOJJ) contact, and grade failure are major predictors, though their relative impact varies based on the severity of offense. Further, being African American loses much of its practical significance in all models once other factors are taken into account. Implications for policy and subsequent research efforts 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.076
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.236
Teacher spread0.216 · 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 designObservational
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
Published2016
Admission routes1
Has abstractyes

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