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Record W4281892930 · doi:10.1037/dev0001403

“A pirate goes nee-nor-nee-nor!” humor with siblings in middle childhood: A window to social understanding?

2022· article· en· W4281892930 on OpenAlexfundno aff
Amy L. Paine, Salim Hashmi, Nina Howe, Nisha Johnson, Matthew Scott, Dale F. Hay

Bibliographic record

VenueDevelopmental Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsnot available
FundersEconomic and Social Research CouncilMedical Research CouncilSocial Sciences and Humanities Research Council of CanadaConcordia UniversityWaterloo Foundation
KeywordsPsychologyPsycINFODevelopmental psychologySocial cognitionCognitive developmentCognitionSiblingSocial cognitive theoryTheory of mind

Abstract

fetched live from OpenAlex

age = 6.91 years, 46.4% female, 98.1% parents identified as English, Welsh, Scottish, or Irish), we conducted detailed observational coding of children's humor production during dress-up play with younger siblings. Focal children also completed a battery of social understanding tasks that measured emotion understanding and second-order belief understanding. Focal children were also observed during solo free play with Playmobil, and their spontaneous references to others' cognitions and play with objects were coded. Correlation analyses indicated that children's word play with their sibling was associated with their tendency to engage in pretense during solo play. Regression analyses showed that humorous sound play with siblings was associated with their emotion understanding and playful teasing with siblings was associated with their spontaneous references to others' cognitive states during solo free play. Our findings contribute to knowledge and theory regarding domains of development associated with humor production in childhood. (PsycInfo Database Record (c) 2022 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 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)0.002
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 score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.102
GPT teacher head0.341
Teacher spread0.239 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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