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Record W3008940866 · doi:10.5817/cp2020-1-2

When you think you know: The effectiveness of restrictive mediation on parental awareness of cyberbullying experiences among children and adolescents

2020· article· en· W3008940866 on OpenAlexaffabout
Oksana Caivano, Karissa Leduc, Victoria Talwar

Bibliographic record

VenueCyberpsychology Journal of Psychosocial Research on Cyberspace · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsMcGill University
Fundersnot available
KeywordsAggressionMediationPsychologyDevelopmental psychologyPerception

Abstract

fetched live from OpenAlex

The current study examined parental awareness of their child’s cyberbullying experiences in relation to the implementation of restrictive mediation strategies (e.g., interaction and technical restrictions) among children in elementary school and adolescents in high school. Canadian parent-child dyads (N = 102) completed a survey where parents reported their perceptions of their child or adolescent’s involvement in cyber aggression, cyber victimization, and witnessing cyber aggression, while children and adolescents (ages 8–16) reported their own experiences. Mean difference scores were calculated to examine parental awareness. The results showed that parents of children in elementary school underestimated their participation in cyber aggression, whereas parents of adolescents in high school overestimated their participation in cyber aggression. In addition, parents of adolescents who did not use restrictive mediation underestimated the extent to which their adolescent witnessed cyber aggression. Overall, this study highlights the importance of parenting practices and parental knowledge of negative online behaviour across childhood and adolescence.

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)0.002
metaresearch head score (Gemma)0.013
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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.044
GPT teacher head0.389
Teacher spread0.345 · 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

Citations35
Published2020
Admission routes2
Has abstractyes

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