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Record W2571890230 · doi:10.1111/cdev.12722

Peer Groups as a Context for School Misconduct: The Moderating Role of Group Interactional Style

2017· article· en· W2571890230 on OpenAlexafffund
Wendy E. Ellis, Lynne Zarbatany, Xinyin Chen, Megan Kinal, Lisa Boyko

Bibliographic record

VenueChild Development · 2017
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMisconductPsychologySocial psychologyPeer groupModerationContext (archaeology)Developmental psychologyStyle (visual arts)Group cohesivenessConversation

Abstract

fetched live from OpenAlex

Abstract Peer group interactional style was examined as a moderator of the relation between peer group school misconduct and group members' school misconduct. Participants were 705 students (Mage = 11.59 years, SD = 1.37) in 148 peer groups. Children reported on their school misconduct in fall and spring. In the winter, group members were observed in a limited-resource task and a group conversation task, and negative and positive group interactional styles were assessed. Multilevel modeling indicated that membership in groups that were higher on school misconduct predicted greater school misconduct only when the groups were high on negative or low on positive interactional style. Results suggest that negative laughter and a coercive interactional style may intensify group effects on children's misconduct.

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)0.018
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.026
GPT teacher head0.308
Teacher spread0.282 · 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

Citations11
Published2017
Admission routes2
Has abstractyes

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