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Record W2057826625 · doi:10.1111/cdev.12349

Children's and Adolescents' Accounts of Helping and Hurting Others: Lessons About the Development of Moral Agency

2015· article· en· W2057826625 on OpenAlexafffund
Holly Recchia, Cecilia Wainryb, Stacia Bourne, Monisha Pasupathi

Bibliographic record

VenueChild Development · 2015
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProsocial behaviorPsychologyAgency (philosophy)NarrativeHelping behaviorHarmPerspective (graphical)Social psychologyMoral developmentDevelopmental psychologyMoral agencyAdolescent developmentPerspective-takingEmpathySociology

Abstract

fetched live from OpenAlex

This study examined children's and adolescents' narrative accounts of everyday experiences when they harmed and helped a friend. The sample included 100 participants divided into three age groups (7-, 11-, and 16-year-olds). Help narratives focused on the helping acts themselves and reasons for helping, whereas harm narratives included more references to consequences of acts and psychological conflicts. With age, however, youth increasingly described the consequences of helping. Reasons for harming others focused especially on the narrator's perspective whereas reasons for helping others were centered on others' perspectives. With age, youth increasingly drew self-related insights from their helpful, but not their harmful, actions. Results illuminate how reflections on prosocial and transgressive experiences may provide distinct opportunities for constructing moral agency.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0040.004
Open science0.0010.003
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 head0.046
GPT teacher head0.301
Teacher spread0.255 · 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 designQualitative
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

Citations54
Published2015
Admission routes2
Has abstractyes

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