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Record W2544521168 · doi:10.1111/cdev.12653

Helping the One You Hurt: Toddlers’ Rudimentary Guilt, Shame, and Prosocial Behavior After Harming Another

2016· article· en· W2544521168 on OpenAlexaff
Jesse D. K. Drummond, Stuart I. Hammond, Emma Satlof‐Bedrick, Whitney E. Waugh, Celia A. Brownell

Bibliographic record

VenueChild Development · 2016
Typearticle
Languageen
FieldPsychology
TopicEmotions and Moral Behavior
Canadian institutionsUniversity of Ottawa
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsShameProsocial behaviorPsychologyDistressHelping behaviorDevelopmental psychologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Abstract This study explored the role of guilt and shame in early prosocial behavior by extending previous findings that guilt- and shame-like responses can be distinguished in toddlers and, for the first time, examining their associations with helping. Toddlers (n = 32; Mage = 28.9 months) were led to believe they broke an adult's toy, after which they exhibited either a guilt-like response that included frequently confessing their behavior and trying to repair the toy; or a shame-like response that included frequently avoiding the adult and seldom confessing or attempting to repair the toy. In subsequent prosocial tasks, children showing a guilt-like response helped an adult in emotional distress significantly faster and more frequently than did children showing a shame-like response.

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)0.005
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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
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 head0.036
GPT teacher head0.282
Teacher spread0.247 · 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

Citations66
Published2016
Admission routes1
Has abstractyes

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