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Record W2891490223 · doi:10.1111/sode.12340

Ability emotional intelligence and children’s behaviour in the playground

2018· article· en· W2891490223 on OpenAlexaff
Pamela Qualter, Itziar Urquijo, S. Peter Henzi, Louise Barrett, Neil Humphrey

Bibliographic record

VenueSocial Development · 2018
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsUniversity of Lethbridge
FundersEconomic and Social Research Council
KeywordsPsychologyAggressionDevelopmental psychologyEarly childhood

Abstract

fetched live from OpenAlex

Abstract We explored whether emotion understanding promotes positive social functioning in childhood using the ability emotional intelligence (AEI) framework, which defines emotion understanding more broadly than is common in developmental science. The prospective study included children ages 9–11 years who completed a measure of AEI at the start of the school year, and whose playground interactions were observed for one full year. Findings showed that, among girls, low AEI was associated with higher levels of direct aggressive behaviour in the playground; boys and girls high or low in AEI were more likely than their peers to watch others during playground social interactions. Further, higher AEI was associated with indirect aggression in school, suggesting higher AEI during childhood may be associated with the developmental transition from direct to indirect forms of aggression. The implications of the findings for school practice in relation to the teaching of emotion understanding are discussed.

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 score0.007
Threshold uncertainty score0.014

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 communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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