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Record W2196455177 · doi:10.1177/0829573515619623

Coping While Connected

2015· article· en· W2196455177 on OpenAlexaff
Chloe C. Hudson, Laura J. Lambe, Debra Pepler, Wendy Craig

Bibliographic record

VenueCanadian Journal of School Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsYork UniversityQueen's University
Fundersnot available
KeywordsCoping (psychology)PsychologyHuman factors and ergonomicsLimitingSuicide preventionClinical psychologyInjury preventionDevelopmental psychologyOccupational safety and healthPoison controlCoping behaviorMedicineMedical emergency

Abstract

fetched live from OpenAlex

The current study explored online preventive coping (privacy settings) and reactive coping (reporting tools) among youth and how the use of these online safety tools related to the frequency of cybervictimization. Surveys were administered to youth in elementary, secondary, and post-secondary school. Results indicated that the prevalence of cybervictimization decreased with grade and did not vary by gender. Gender differences emerged in the use of coping mechanisms, with females more likely to use privacy settings than males. In addition, older females were more likely to use privacy settings compared with younger females. Compared with older females, younger females were more likely to use informal reporting tools (i.e., contacting the person responsible for posting inappropriate content). Only limiting the visibility of posts and comments was uniquely associated with a lower frequency of cybervictimization. Younger adolescents were less likely to engage in the only behaviour that was associated with less frequent cybervictimization. These findings suggest that younger youth may lack the skills and knowledge necessary to cope effectively with cybervictimization.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.074
GPT teacher head0.340
Teacher spread0.266 · 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.

Study designNot applicable
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

Citations14
Published2015
Admission routes1
Has abstractyes

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