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Record W2552665100 · doi:10.1111/cdev.12632

Children's Sympathy, Guilt, and Moral Reasoning in Helping, Cooperation, and Sharing: A 6-Year Longitudinal Study

2016· article· en· W2552665100 on OpenAlexaff
Tina Malti, Sophia F. Ongley, Joanna Peplak, Maria Paula Chaparro, Marlis Buchmann, Antonio Zuffianò, Lixian Cui

Bibliographic record

VenueChild Development · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Toronto
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsSympathySadnessPsychologySocial psychologyFeelingProsocial behaviorHelping behaviorMoral disengagementDevelopmental psychologyAnger

Abstract

fetched live from OpenAlex

Abstract This study examined the role of sympathy, guilt, and moral reasoning in helping, cooperation, and sharing in a 6-year, three-wave longitudinal study involving 175 children (Mage 6.10, 9.18, and 12.18 years). Primary caregivers reported on children's helping and cooperation; sharing was assessed behaviorally. Child sympathy was assessed by self- and teacher reports, and self-attributed feelings of guilt–sadness and moral reasoning were assessed by children's responses to transgression vignettes. Sympathy predicted helping, cooperation, and sharing. Guilt–sadness and moral reasoning interacted with sympathy in predicting helping and cooperation; both sympathy and guilt–sadness were associated with the development of sharing. The findings are discussed in relation to the emergence of differential motivational pathways to helping, cooperation, and sharing.

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)0.003
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 score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.027
GPT teacher head0.279
Teacher spread0.252 · 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

Citations105
Published2016
Admission routes1
Has abstractyes

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