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Record W2107154644 · doi:10.1037/a0037689

Loneliness in Chinese children across contexts.

2014· article· en· W2107154644 on OpenAlexfundno aff
Xinyin Chen, Li Wang, Dan Li, Junsheng Liu

Bibliographic record

VenueDevelopmental Psychology · 2014
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaNational Natural Science Foundation of China
KeywordsLonelinessPsychologyShynessDevelopmental psychologyContext (archaeology)ChinaAggressionSocial environmentDemographySocial psychologyAnxietyGeographyPsychiatry

Abstract

fetched live from OpenAlex

This study examined loneliness and its associations with social functioning in children across different historical times and contexts in China. We collected data from urban and rural groups of school-age children (N = 2,588; M age = 10 years) using self-reports and peer assessments. The results indicated that children in 2002 and 2005 urban groups had lower scores on loneliness than did children in 1992 and 1998 urban groups, suggesting that as urban China became a more modernized, self-oriented society, children tended to report lower levels of loneliness. Consistent with this trend, urban children reported lower levels of loneliness than did their rural counterparts in recent years. The analysis of associations between social functioning and loneliness revealed that across groups, sociability was negatively associated with loneliness, and aggression was positively associated with loneliness. The association between shyness and loneliness differed among the groups; it was negative in the 1992 urban group, positive in the 2002 and 2005 urban groups, and nonsignificant in the 1998 urban and 2007 rural groups. The different associations suggest that whether shy children feel lonely might depend on context.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.999

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)0.0020.002

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 head0.053
GPT teacher head0.425
Teacher spread0.372 · 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; both teacher heads agree on what is shown here.

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

Citations53
Published2014
Admission routes1
Has abstractyes

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