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Record W2135201880 · doi:10.1080/17405620701632069

Good friendships, bad friends: Friendship factors as moderators of the relation between aggression and social information processing

2007· article· en· W2135201880 on OpenAlexaff
Julie C. Bowker, Kenneth H. Rubin, Linda Rose‐Krasnor, Cathryn Booth‐LaForce

Bibliographic record

VenueEuropean Journal of Developmental Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsBrock University
Fundersnot available
KeywordsFriendshipAggressionPsychologyDevelopmental psychologyCoping (psychology)Peer groupSocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

The primary objective was to examine whether the associations between aggression and social information processing was moderated by friendship quality and the aggressiveness of the best friend. Drawn from a larger normative sample of 5th and 6th graders, 385 children (180 boys) completed questionnaires pertaining to friendship quality and social information processing. Friendship and peer nominations of behaviours were collected. Results revealed positive associations between aggressive behaviour and the endorsement of aggressive coping strategies in cases where the protagonist was an unfamiliar peer. However, one important exception emerged: no significant associations between aggression and aggressive coping were revealed for children with high-quality friendships with aggressive peers. In cases where the protagonist was the best friend, there was a significant relation between aggression and vengeful coping, but only for those participants who had a low-quality friendship with an aggressive friend.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.030
GPT teacher head0.306
Teacher spread0.276 · 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

Citations27
Published2007
Admission routes1
Has abstractyes

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