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Record W2317341792 · doi:10.1037/a0035191

The role of moral emotions in the development of children’s sharing behavior.

2013· article· en· W2317341792 on OpenAlexfundno aff
Sophia F. Ongley, Tina Malti

Bibliographic record

VenueDevelopmental Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSympathyProsocial behaviorPsychologyFeelingDictator gameSocial psychologyDevelopmental psychologyMoral development

Abstract

fetched live from OpenAlex

This study investigated the role of moral emotions in the development of children's sharing behavior (N = 244 4-, 8-, and 12-year-old children). Children's sympathy was measured with both self- and primary caregiver-reports, and participants anticipated their negatively and positively valenced moral emotions (i.e., feeling guilty, sad, or bad; and feeling proud, happy, or good) following actions that either violated or upheld moral norms. Sharing was measured through children's allocation of resources in the dictator game. Children's self-reported sympathy emerged as a significant predictor of sharing in early childhood. For children with low levels of sympathy, sharing was also predicted by negatively valenced moral emotions following the failure to perform prosocial actions. In addition, results demonstrated an age-related increase in sharing for boys between ages 4 and 8 and a decrease in sharing for boys between ages 8 and 12. We discuss the findings in relation to the emergence of 2 compensatory emotional pathways to sharing, 1 via sympathy and 1 via negatively valenced moral emotions.

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)0.006
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.338
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 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

Citations132
Published2013
Admission routes1
Has abstractyes

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