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Record W4213147587 · doi:10.2991/assehr.k.220131.087

High School Cyberbullying and Adolescents’ Depression in China

2022· article· en· W4213147587 on OpenAlexaff
Yihan Zhang, Zixi Zhang

Bibliographic record

VenueAdvances in Social Science, Education and Humanities Research/Advances in social science, education and humanities research · 2022
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsMcMaster University
Fundersnot available
KeywordsDepression (economics)ChinaPsychologyComputer securityInternet privacyComputer sciencePolitical science

Abstract

fetched live from OpenAlex

This paper focused on cyberbullying in a Chinese context and its relationship with Chinese adolescents' depression, self-harm, and suicidal ideation based on past literature.Mainly, five articles related to this topic were analyzed respectively to give a comprehensive discussion.The literature suggested that cyberbullying is prevalent among adolescents in diverse Chinese societies and cyberbullying is positively correlated with several negative mental states for Chinese adolescents.However, none of the research can draw a causal conclusion, indicating more longitudinal tracking designs as a future direction.Furthermore, therapy and advice on preventing cyberbullying and depression are suggested based on conclusions drawn.Research in China should further investigate cyberbullying from the social-ecological perspectives and explore more concerning the prevention tactic and potential intervention about cyberbullying.

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)0.001
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 score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.445
Teacher spread0.390 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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