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Record W2063136938 · doi:10.1037/0012-1649.43.6.1484

Developmental changes in gender composition of friendship networks in adolescent girls and boys.

2007· article· en· W2063136938 on OpenAlexafffund
François Poulin, Sara Pedersen

Bibliographic record

VenueDevelopmental Psychology · 2007
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversité de MontréalResearch Unit on Children's Psychosocial MaladjustmentUniversité du Québec à Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFriendshipPsychologyDevelopmental psychologyClosenessNormativeContext (archaeology)Adolescent developmentSocial psychology

Abstract

fetched live from OpenAlex

This article describes both normative changes and individual differences in the gender composition of girls' and boys' friendship networks across adolescence and predicts variations in these changes. It also examines changes in the characteristics (context, age difference, closeness, and support) of same- and other-sex friendships in the network. Girls and boys (N=390) were interviewed annually from Grades 6 to 10 (76% retention). Growth in the proportion of other-sex friends was significantly more pronounced for girls and was related to different predictors for girls and boys. Moreover, over time, girls had other-sex friends that were increasingly older than themselves, and most of these friendships took place outside of the school, which was not the case for boys. Growth in the proportion of other-sex friends was more pronounced for secondary than for best friends. Finally, both girls and boys reported receiving higher levels of help from girls than from boys. These findings suggest that other-sex friendships might place some of the girls on a problematic developmental trajectory.

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)0.000
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.046
GPT teacher head0.332
Teacher spread0.286 · 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

Citations195
Published2007
Admission routes2
Has abstractyes

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