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Record W2098900934 · doi:10.5539/ass.v10n8p1

Taiwanese Adolescents’ Self-Disclosures on Private Section of Facebook, Trusts in and Intimacy with Friends in Different Close Relationships

2014· article· en· W2098900934 on OpenAlexvenueno aff
Shih Hsiung Liu

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
FundersNational Science Council
KeywordsClosenessPsychologySelf-disclosureVariance (accounting)Social psychologyStructural equation modelingCluster samplingFriendshipBusinessSociology

Abstract

fetched live from OpenAlex

The study investigates self-disclosure by adolescents in Taiwan on the private section of Facebook, and their trust in, and intimacy with, Facebook friends in different close relationships. This study further determines the predictors of intimate self-disclosure that are mediated by trust in Facebook friends. In total, 1370 Taiwanese adolescents, via stratified random cluster sampling, filled out the validated questionnaire between March and May 2013. One-way repeated measures analysis of variance and structural equation modeling were applied to analyze data on self-disclosure, intimacy, and trust, respectively, among five levels of Facebook friends. The study demonstrates that as the closeness of friends’ increases, the amount of self-disclosure, intimacy, and trust increases. Additionally, the level of self-disclosure can predict the level of intimacy with Facebook friends. Adolescents’ trust in friends in close relationships may strengthen the development of intimacy; however, there is no such reaction in the group of unfamiliar Facebook friends.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.013
GPT teacher head0.286
Teacher spread0.273 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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