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Record W1538938587 · doi:10.3389/fpsyg.2015.00552

Why be moral? Children's explicit motives for prosocial-moral action

2015· article· en· W1538938587 on OpenAlexaff
Sonia Sengsavang, Kayleen Willemsen, Tobias Krettenauer

Bibliographic record

VenueFrontiers in Psychology · 2015
Typearticle
Languageen
FieldPsychology
TopicPsychological Testing and Assessment
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsProsocial behaviorPsychologyMoralityMoral developmentSocial psychologyExplicationAction (physics)Developmental psychologyMoral disengagementSocial cognitive theory of moralityMoral reasoningMoral behaviorInternalism and externalismEpistemology

Abstract

fetched live from OpenAlex

Recent research on young children's morality has stressed the autonomous and internal nature of children's moral motivation. However, this research has mostly focused on implicit moral motives, whereas children's explicit motives have not been investigated directly. This study examined children's explicit motives for why they want to engage in prosocial actions and avoid antisocial behavior. A total of 195 children aged 4-12 years were interviewed about their motives for everyday prosocial-moral actions, as well as reported on their relationship with their parents. Children's explicit motives to abstain from antisocial behavior were found to be more external and less other-oriented than their motives for prosocial action. Motives that reflected higher levels of internal motivation became more frequent with age. Moreover, positive parent-child relationships predicted more other-oriented motives and greater explication of moral motives. Overall, the study provides evidence that children's explicit moral motivation is far more heterogeneous than prominent theories of moral development (past and present) suggest.

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 score0.004
Threshold uncertainty score0.008

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 studies0.0000.001
Scholarly communication0.0010.001
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 head0.163
GPT teacher head0.422
Teacher spread0.260 · 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
Published2015
Admission routes1
Has abstractyes

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