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Record W4393091331 · doi:10.3389/fdpys.2024.1369085

When best friendships end: young adolescents' responses to hypothetical best friendship dissolution and associations with real-life friendship outcomes

2024· article· en· W4393091331 on OpenAlexaff
Julie C. Bowker, Jenna P. Weingarten, Rebecca G. Etkin, Melanie A. Dirks

Bibliographic record

VenueFrontiers in Developmental Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsFriendshipPsychologyDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

Introduction This study examined young adolescents' responses to two types of hypothetical best friendship dissolution (complete and downgrade dissolutions). Responses included their attributions, emotional reactions, and coping strategies. It also considered whether responses vary across dissolution type and are related to the real-life friendship-specific outcomes of best friendship dissolution and friendship quantity. Method Data were collected from 318 young adolescents at two time points (Time 1 (T1): M age = 11.87 years) and included a newly-developed vignette measure of responses to hypothetical complete and downgrade dissolutions (T1), real-life complete and downgrade dissolutions experienced by participants (T2), and friendship (T1, T2). Results Findings showed that adolescents responded differently in their emotional reactions and coping strategies to hypothetical complete and downgrade dissolutions. Path models revealed unique linkages between several responses, such as vengeful coping and the real-life friendship-specific outcomes. Discussion Findings suggest variability in how young adolescents respond to hypothetical best friendship dissolutions and that such variability may explain differences in their real-life friendships.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.321
Teacher spread0.284 · 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.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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