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Record W2119716728 · doi:10.1080/00221320209598697

Motivation to Hide Emotion and Children's Understanding of the Distinction Between Real and Apparent Emotions

2002· article· en· W2119716728 on OpenAlexaff
Pierre Gosselin, Madeleine Warren, MichèLe Diotte

Bibliographic record

VenueThe Journal of Genetic Psychology · 2002
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProsocial behaviorPsychologyFacial expressionSocial psychologyDevelopmental psychologyFace (sociological concept)Masking (illustration)Emotion classificationCognitive psychologyCommunicationLinguistics

Abstract

fetched live from OpenAlex

The authors investigated the extent to which children's understanding of the distinction between real and apparent emotions varied according to the motivation to hide emotions. Children, aged 6-7 and 10-11 years, were read stories designed to elicit either prosocial or self-protective motivated display rules and were asked to predict the facial expressions the protagonists would make to hide felt emotions. Children were found to understand the distinction between real and apparent emotions very well, independently of the type of motivation. Contrary to predictions, boys understood this distinction better than did girls when the motivation to hide positive emotions was prosocial. Children perceived neutralization as the most appropriate strategy to hide felt emotions, followed by masking.

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)0.000
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 score0.040
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.065
GPT teacher head0.299
Teacher spread0.233 · 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

Citations41
Published2002
Admission routes1
Has abstractyes

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