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Record W3016729865 · doi:10.3138/jcfs.51.1.005

Do Only-Children Communicate Better Than Non-Only Children?

2020· article· en· W3016729865 on OpenAlexvenueno aff
Wei Wang, Zhang Jie, Dwight A. Hennessy, Wenqiang Yin

Bibliographic record

VenueJournal of Comparative Family Studies · 2020
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsnot available
Fundersnot available
KeywordsInterpersonal communicationPsychologySiblingDevelopmental psychologyChinaScale (ratio)Interpersonal relationshipSocial communicationSocial psychologyGeography

Abstract

fetched live from OpenAlex

Since the implementation of the One-Child Police in China in 1979, great concern has been raised about the physical and psychological development of “only-children.” Some researchers believe that only-children may have difficulty with social skills, which include communication ability, because they would lack early sibling interactions. The aim of the present study was to explore the communication ability of only-children compared to children raised with siblings. We administered the self-developed Interpersonal Communication Ability Assessment Scale, which had been previously validated and refined, to 1,376 medical students in China. Results showed that when considering communication ability on its own, there were slight differences found between only-children and non-only-children. However, this difference was no longer significant when other independent variables were included in a hierarchical linear regression. This might be due to the fact that only-children have more highly educated parents, with more high-status careers, and greater family income that might provide greater social and educational opportunities, which might then increase communication abilities.

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)0.005
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.143
GPT teacher head0.388
Teacher spread0.245 · 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

Citations6
Published2020
Admission routes1
Has abstractyes

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