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Record W4399593545 · doi:10.1080/02699931.2024.2362371

Parental linguistic content and distancing predict beliefs about emotion and child emotion regulation

2024· article· en· W4399593545 on OpenAlexafffund
Chelsea Reaume, Kristel Thomassin

Bibliographic record

VenueCognition & Emotion · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDistancingPsychologyAngerDevelopmental psychologyEmotion workCognitive reappraisalNegative emotionEmotion classificationExpressed emotionSocial distanceCognitionExpression (computer science)Social psychologyCoronavirus disease 2019 (COVID-19)

Abstract

fetched live from OpenAlex

Employing a constructionist framework of emotion, this study examines whether parental language during emotion belief discussions predicts parents' self-reported beliefs about emotion and child emotion regulation (ER). 102 parents of children ages 8 through 12 participated in focus groups about emotion beliefs, and nine months later, completed questionnaires on their emotion beliefs and child ER. Focus group content was analyzed for positive and negative emotion talk, cognitive process talk, and an established linguistic marker of psychological distancing. Parents' positive emotion talk and parental linguistic distancing when discussing their child's (but not their own) emotion experiences positively predicted beliefs about children's emotional capabilities. Finally, negative emotion talk negatively predicted parental beliefs about children's capacity to control their own emotions and the value of anger expression as well as child ER. Current findings contribute to our understanding of how parental communication patterns about emotions may influence emotion beliefs and child emotion development.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score0.964

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 head0.025
GPT teacher head0.269
Teacher spread0.243 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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