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Record W2132734119 · doi:10.1177/0143034308099201

Some of My Best Friends——Experiences of Bullying Within Friendships

2008· article· en· W2132734119 on OpenAlexaff
Faye Mishna, Judith Wiener, Debra Pepler

Bibliographic record

VenueSchool Psychology International · 2008
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsYork UniversityUniversity of Toronto
FundersFrancis Crick Institute
KeywordsFriendshipPsychologyDevelopmental psychologyQualitative researchPerceptionSocial psychologyExploratory researchAggressionHuman factors and ergonomicsSuicide preventionPoison controlMedicine

Abstract

fetched live from OpenAlex

This study provides one of the first assessments of bullying among friends based on the perceptions of victimized children and their parents and teachers, with respect to actual situations that they raised for discussion. The qualitative methodology privileges the `lived experience' of study participants. Interviews were conducted with children in 4th and 5th grades who self-identified as victimized, and with their parents and teachers. Bullying by a child considered a friend can be particularly confusing. It can be difficult for the child to recognize that a friend is bullying and for parents and teachers to identify these interactions as bullying. Themes that emerged included the child's awareness of being bullied by friends; the adults' awareness that the child was bullied by friends; impact on the friendship and differentiating bullying from conflict in friendship. This exploratory research suggests that bullying among friends is an important issue that demands further investigation.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.043
GPT teacher head0.351
Teacher spread0.308 · 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 designQualitative
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

Citations130
Published2008
Admission routes1
Has abstractyes

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