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Record W2087128431 · doi:10.1080/02568540009594759

Hyperbole and Humor in Children's Language Play

2000· article· en· W2087128431 on OpenAlexaff
Donna Varga

Bibliographic record

VenueJournal of Research in Childhood Education · 2000
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsMount Saint Vincent University
Fundersnot available
KeywordsPsychologyHyperboleDevelopmental psychologyContext (archaeology)Scope (computer science)AssertionLinguisticsCognitionLanguage developmentCognitive psychology

Abstract

fetched live from OpenAlex

This study examines the processes by which preschool children initiate, organize, and maintain language play interactions. The language behaviors examined are verbal hyperbole, whereby play interactions are initiated by a child's assertion of extraordinary ability, with subsequent responses between participants enlarging the scope of the claim. As children voice incongruities of greater proportion, the emotional climate of the play is heightened and ingenious verbal representations are provoked. From the analysis, developmental features of hyperbolic language play are identified, and language play is contextualized within the broader context of children's social play interactions. The observations portray the wide scope of 4- and 5-year-olds' abilities to engage in conceptual language play activity, with 4- and 5-year-olds differing from each other, and potentially in advance of traditionally expected preoperational abilities.

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 score0.004
Threshold uncertainty score0.009

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 studies0.0010.002
Scholarly communication0.0020.001
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 head0.022
GPT teacher head0.384
Teacher spread0.362 · 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

Citations44
Published2000
Admission routes1
Has abstractyes

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