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High School Social Climate and Antisocial Behavior: A 10 Year Longitudinal and Multilevel Study

2008· article· en· W2013505663 on OpenAlexaff
Line Leblanc, Raymond R. Swisher, Frank Vitaro, Richard E. Tremblay

Bibliographic record

VenueJournal of Research on Adolescence · 2008
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsPsychologyMultilevel modelSchool climateLongitudinal studyDevelopmental psychologyMathematics education

Abstract

fetched live from OpenAlex

A longitudinal and multilevel approach is used to examine the relationship between antisocial behavior during adolescence and high school social climate. The data are taken from a longitudinal study of 1,233 boys and girls who attended 217 public and private high schools. Students' disruptive behaviors were assessed yearly from 6 to 12 years of age. High school social climate was assessed by teachers, and students reported on their violent and nonviolent antisocial behavior while in high school. The multilevel analyses revealed (1) a large difference between the percentage of variance explained within schools (97%), compared with between schools (3%), and (2) teachers' reports of classroom behavior problems explain between‐school differences in student reported antisocial behavior, after controlling for students' family adversity and history of behavior problems during elementary school. The theoretical and practical implications of the study are examined and future directions for research 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 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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.003
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 head0.126
GPT teacher head0.442
Teacher spread0.316 · 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 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

Citations41
Published2008
Admission routes1
Has abstractyes

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