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Record W3137792441 · doi:10.1002/pits.22512

School climate and bystander responses to bullying

2021· article· en· W3137792441 on OpenAlexaffabout
Chiaki Konishi, Shelley Hymel, Tracy K. Y. Wong, Terry Waterhouse

Bibliographic record

VenuePsychology in the Schools · 2021
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsSimon Fraser UniversityUniversity of British ColumbiaMcGill University
Fundersnot available
KeywordsBystander effectPsychologyDiversity (politics)School climateMultilevel modelDevelopmental psychologyPerceptionSocial psychologyPedagogy

Abstract

fetched live from OpenAlex

Abstract This study examined the association between school climate and bystander responses to bullying. Participants included 26,176 secondary students (grades 8–12; 13,224 girls) from 76 schools across Western Canada, who were asked to complete a self‐reported, district‐wide, school‐based survey. Results from a contextual effects model in a two‐level multilevel modeling framework revealed that certain aspects of school climate significantly predicted different types of bystander behaviors. Specifically, greater student reports of school safety, adult support/respect, adult responsiveness, and adult/student acceptance of diversity predicted more active intervening behaviors. In addition, greater student perceptions of school safety, adult support/respect, and adult acceptance of diversity predicted less avoiding or neglecting behaviors. Interestingly, peer support, school belonging, student acceptance of diversity, and provision of opportunities were negatively related to seeking support from adults. This study underscores the unique and critical role of school climate on bystander behaviors among adolescents.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.372
Teacher spread0.333 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

Explore more

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