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Record W2904192831 · doi:10.1037/dev0000654

Getting help for others: An examination of indirect helping in young children.

2018· article· en· W2904192831 on OpenAlexfundno aff
Tara A. Karasewich, Valerie A. Kuhlmeier, Jonathan S. Beier, Kristen A. Dunfield

Bibliographic record

VenueDevelopmental Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProsocial behaviorPsychologyPsycINFOCognitionDevelopmental psychologySocial cognitionAgency (philosophy)Sense of agencyTask (project management)Helping behaviorAction (physics)Cognitive developmentSocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

When young children recruit others to help a person in need, media reports often treat it as a remarkable event. Yet it is unclear how commonly children perform this type of pro-social behavior and what forms of social understanding, cognitive abilities, and motivational factors promote or discourage it. In this study, 48 three- to four-year-old children could choose between two actors to retrieve an out-of-reach object for a third person; during this event, one actor was physically unable to provide help. Nearly all of children's responses appropriately incorporated the actors' action capacities, indicating that rational prosocial reasoning-the cognitive basis for effective indirect helping-is common at this young age. However, only half of children actually directed an actor to help, suggesting that additional motivational factors constrained their prosocial actions. A behavioral measure of social inhibition and within-task scaffolding that increased children's personal involvement were both strongly associated with children's initiation of indirect helping behavior. These results highlight social inhibition and recognizing one's own potential agency as key motivational challenges that children must overcome to recruit help for others. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.352
Threshold uncertainty score0.847

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.035
GPT teacher head0.328
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 teacher head, 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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