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Record W2364148882 · doi:10.1080/00221325.2016.1174100

Assessment and Implications of Social Withdrawal Subtypes in Young Chinese Children: The Chinese Version of the Child Social Preference Scale

2016· article· en· W2364148882 on OpenAlexaff
Yan Li, Jingjing Zhu, Robert J. Coplan, Zhu-Qing Gao, Pin Xu, Linhui Li, Huimin Zhang

Bibliographic record

VenueThe Journal of Genetic Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsSocioemotional selectivity theoryShynessPsychologyDevelopmental psychologySocial withdrawalScale (ratio)PreferenceAnxietyPsychiatry

Abstract

fetched live from OpenAlex

The authors' goals were to evaluate the psychometric properties of the Chinese version of the Child Social Preference Scale (CSPS; R. J. Coplan, K. Prakash, K. O'Neil, & M. Armer, 2004) and examine the links between both shyness and unsociability and indices of socioemotional functioning in young Chinese children. Participants included of two samples recruited from kindergarten classes in two public schools in Shanghai, China. Both samples included children 3-5 years old (Sample 1: n = 350, Mage = 4.72 years, SD = 0.58 years; Sample 2: n = 129, Mage = 4.40 years, SD = 0.58 years). In both samples, mothers rated children's social withdrawal using the newly created Chinese version of the CSPS, and in Sample 2, teachers also provided ratings of socioemotional functioning. Consistent with previous findings from other cultures, results from factor analyses suggested a 2-factor model for the CSPS (shyness and unsociability) among young children in China. In contrast to findings from North America, child shyness and unsociability were associated with socioemotional difficulties in kindergarten. Some gender differences were also noted. Results are discussed in terms of the assessment and implications of social withdrawal in early childhood in China.

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 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 score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.305
Teacher spread0.294 · 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.

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

Citations46
Published2016
Admission routes1
Has abstractyes

Explore more

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