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Record W3089161722 · doi:10.5539/ies.v13n10p47

Moral Disengagement, Attitudes Towards Violence and Irrational Beliefs as Predictors of Bullying Cognition in Adolescence

2020· article· en· W3089161722 on OpenAlexvenueno aff
Metin Kocatürk, Tuğba Türk Kurtça

Bibliographic record

VenueInternational Education Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionMoral disengagementContext (archaeology)Developmental psychologyDisengagement theoryScale (ratio)Multilevel modelSocial psychologyIrrational numberPoison controlClinical psychologyPsychiatry

Abstract

fetched live from OpenAlex

Considering the causes of bullying behavior, the situations caused by it and its impact area, the formation of bullying in the cognitive dimension draws attention. In this context, examination of thoughts or cognition about bullying becomes an important element in explaining bullying. In this study, it is aimed to examine moral disengagement tendencies, attitudes towards violence and irrational beliefs as predictors of bullying cognition of adolescents between the ages of 15-18. Study group consisted of 369 individuals, 197 females and 172 males. Bullying Cognition Scale, Moral Disengagement Scale, Attitude Towards Violence Scale and Irrational Beliefs Scale for adolescents were applied to participants. The data obtained were tested by hierarchical regression analysis. Moral disengagement tendency, attitudes towards violence and irrational beliefs (demands for success and for comfort sub-dimensions) predicted cognition about bullying significantly. It was determined as a result of hierarchical regression analysis that these variables predicted bullying cognition both separately and together. At the end of the study, suggestions for the studies to be carried out for bullying, which would be handled within the scope of cognitive structure, were presented.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.073
GPT teacher head0.391
Teacher spread0.318 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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