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Record W2911311068 · doi:10.1111/sjop.12516

Bystanders’ affect toward bully and victim as predictors of helping and non‐helping behaviour

2019· article· en· W2911311068 on OpenAlexaffabout
Jessica Trach, Shelley Hymel

Bibliographic record

VenueScandinavian Journal of Psychology · 2019
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychologyAffect (linguistics)Helping behaviorSocial psychologyHelping handCommunication

Abstract

fetched live from OpenAlex

The current study examined how children's relationship with the bully and victim impacted their reactions as bystanders. An ethnically diverse sample of 2,513 Canadian students in grades 4-7 responded to questions about their experiences of bullying, including the frequency with which they witnessed bullying at school. Approximately 89% of the sample reported witnessing bullying at school during the current school year. Subsequently, participants were asked to recall a specific bullying incident that they witnessed and describe: (1) their relationship with the bully and victim; (2) how they felt while witnessing; and (3) how they responded as a bystander. Compared to situations where they didn't know the victim, bystanders were more likely to intervene directly (e.g., try to stop the bully, comfort the victim) if they liked the victim, and less likely to tell an adult if they disliked the victim. Aggressive intervention was more common if the witness didn't like the bully, but also if they didn't like the victim compared to if they didn't know them. Regarding emotions, anger emerged as an especially powerful predictor of bystander defending, with youth being over five times more likely to try to stop the bullying or comfort the victim if they felt angry. Implications of these findings for the development of ecologically valid, anti-bullying interventions are discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.018
GPT teacher head0.316
Teacher spread0.298 · 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.

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

Citations31
Published2019
Admission routes2
Has abstractyes

Explore more

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