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Record W2040558989 · doi:10.1080/10926480903310286

<i>“That Was Smooth, Mom”:</i> Children's Production of Verbal and Gestural Irony

2009· article· en· W2040558989 on OpenAlexafffund
Penny M. Pexman, Lenka Zdrazilova, Devon McConnachie, Kirby Deater‐Deckard, Stephen A. Petrill

Bibliographic record

VenueMetaphor and Symbol · 2009
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Sydney
KeywordsIronyPsychologyNonverbal communicationContext (archaeology)Developmental psychologyLinguisticsHistoryPhilosophy

Abstract

fetched live from OpenAlex

Research suggests that typically developing children begin to understand verbal irony around 5 or 6 years of age. Children's production of verbal irony, however, has not previously been examined. This study was a preliminary investigation of children's irony production, including both verbal and gestural (nonverbal) forms. We coded instances of irony in interactions within 118 family triads, each consisting of 1 parent and 2 children, aged 3 to 15 years. Triads performed an 8-min cooperative task with dominos. In this context, gestural irony was used more frequently than verbal irony: 80% of the families used gestural irony at least once and 32% used verbal irony at least once. Children produced gestural irony as early as 4 years of age, and verbal irony as early as 5 years of age. Children's use of irony was not related to their general cognitive ability or vocabulary, but was related to use of irony by other members of the triad. Results suggest that social context is important to the emergence of irony production.

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)0.003
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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.257
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 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

Citations68
Published2009
Admission routes2
Has abstractyes

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