MétaCan
Menu
Back to cohort
Record W2136228328 · doi:10.1017/s0305000909009520

Development of children's ability to distinguish sarcasm and verbal irony*

2009· article· en· W2136228328 on OpenAlexaff
Melanie Glenwright, Penny M. Pexman

Bibliographic record

VenueJournal of Child Language · 2009
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversity of CalgaryUniversity of Manitoba
Fundersnot available
KeywordsSarcasmIronyPsychologyLiteral and figurative languageLiteral (mathematical logic)PragmaticsLinguisticsDevelopmental psychology

Abstract

fetched live from OpenAlex

Adults distinguish between ironic remarks directed at targets (sarcasm) and ironic remarks not directed at specific targets. We investigated the development of children's appreciation for this distinction by presenting these speech acts to 71 five- to six-year-olds and 71 nine- to ten-year-olds. Five- to six-year-olds were beginning to understand the non-literal meanings of sarcastic speakers and ironic speakers but did not distinguish ironic and sarcastic speakers' intentions. Nine- to ten-year-olds were more accurate at understanding sarcastic and ironic speakers and they distinguished these speakers' intentions, rating sarcastic criticisms as more 'mean' than ironic criticisms. These results show that children can determine the non-literal meanings of sarcasm and irony by six years of age but do not distinguish the pragmatic purposes of these speech acts until later in middle childhood.

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)0.004
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.280
Teacher spread0.271 · 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

Citations144
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Child LanguageSame topicLanguage, Metaphor, and CognitionFrench-language works237,207