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Record W3118080854 · doi:10.18357/ijcyfs114.2202019986

CYBERAGGRESSION: THE EFFECT OF PARENTAL MONITORING ON BYSTANDER ROLES

2020· article· en· W3118080854 on OpenAlexvenueno aff
Michal Levy, Revital Sela‐Shayovitz

Bibliographic record

VenueInternational Journal of Child Youth and Family Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsBystander effectSupporterParental monitoringPsychologyAggressionDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

The digital world has created new opportunities for aggression through cyberaggression. Despite growing research interest in cyberaggression, little is known about the various bystander roles in the digital interaction. This paper examines the effect of parental monitoring practices (parental restriction, youth disclosure, and parental solicitation) on five bystander roles: aggressor-supporter, defender, help-seeker, outsider, and passive bystander. Data were derived from self-report questionnaires answered by a sample of 501 adolescents in Israel. The findings indicate that adolescents who share their experiences of cyberaggression with their parents are more likely than others to defend the cybervictim. Interaction effects were found between adolescent gender, installing warning applications, parent gender, and the aggressor-supporter role. Boys whose parents installed warning applications and whose fathers monitored their online activities were positively associated with the aggressor-supporter role, while girls who were higher aggressor-supporter reported that their parents used warning applications but did not monitor their online activities. The discussion focuses on the theoretical and practical implications of the effectiveness of parental monitoring on the cyberaggression bystander’s role.

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)0.000
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 score0.035
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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