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Record W3086317388 · doi:10.1111/bjdp.12347

Shyness and empathy in early childhood: Examining links between feelings of empathy and empathetic behaviours

2020· article· en· W3086317388 on OpenAlexaff
Federica Zava, Stefania Sette, Emma Baumgartner, Robert J. Coplan

Bibliographic record

VenueBritish Journal of Developmental Psychology · 2020
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsShynessEmpathyPsychologyProsocial behaviorFeelingDevelopmental psychologySocial psychologyAnxiety

Abstract

fetched live from OpenAlex

Although shy children have been described as less empathetic than their more sociable peers, this may be due to a performance rather than a competence deficit. The aim of this study was to explore the moderating role of shyness in the association between empathic feelings and empathic‐related reactions. Participants were 212 preschoolers ( M age = 58.32 months, SD = 10.72). Children provided self‐reports of empathetic feelings, parents rated child shyness and empathic behaviours (e.g., reparative behaviours), and teachers assessed indices of socio‐emotional functioning (e.g., prosocial behaviours). Results revealed interaction effects between empathic feelings and shyness in the prediction of outcome variables. Among children with lower levels of shyness, empathy rated by children was positively related to empathetic and reparative behaviours (rated by parents) and prosocial behaviours (rated by teachers). At higher levels of shyness, these relations were attenuated. These results can be interpreted to suggest that although shy children may not differ from their more sociable counterparts in experiencing empathy, they seem to be less likely to act empathically.

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)0.004
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 score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.285
Teacher spread0.250 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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