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Record W1528934807 · doi:10.1002/jcop.21585

MINORITY IN THE MAJORITY: COMMUNITY ETHNICITY AS A CONTEXT FOR RACIAL BULLYING AND VICTIMIZATION

2013· article· en· W1528934807 on OpenAlexaff
Lyndall Schumann, Wendy Craig, Andrei Rosu

Bibliographic record

VenueJournal of Community Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsQueen's University
Fundersnot available
KeywordsEthnic groupContext (archaeology)Diversity (politics)PsychologyDemographyMedicineGeographySociology

Abstract

fetched live from OpenAlex

The present study explored the relationships between individual ethnicity and community ethnicity factors and the prevalence of racial bullying. It was hypothesized that individuals belonging to the majority ethnic group in a community were less racially victimized than in a community in which they were minority members. Data were collected from 20,021 students in Grades 6 to 10 as part of the 2009/2010 Health Behaviour in School‐aged Children Survey, from Geographical Information Systems data, and from census data. Community diversity was associated with prevalence of racial victimization, although relationships differed by type of religious organization. Those of East/Southeast Asian, Caucasian, and South Asian ethnicity were more likely to be racially victimized in communities in which they were the minority ethnic group than when they were in the majority group. The importance of considering ethnicity characteristics that are related to racially focused bullying in the community context is 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 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 score0.021
Threshold uncertainty score0.041

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 studies0.0030.002
Scholarly communication0.0020.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.064
GPT teacher head0.381
Teacher spread0.317 · 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

Citations45
Published2013
Admission routes1
Has abstractyes

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