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Record W4407710430 · doi:10.1017/s0305000925000054

Learning Irony in School: Effects of Metapragmatic Training

2025· article· en· W4407710430 on OpenAlexaff
Henri Olkoniemi, Tuomo Häikiö, Milla Merinen, Jasmiina Manninen, Matti Laine, Penny M. Pexman

Bibliographic record

VenueJournal of Child Language · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsWestern University
FundersKulttuurin ja Yhteiskunnan Tutkimuksen ToimikuntaUniversity of Cambridge
KeywordsIronyPsychologyComprehensionReading comprehensionLinguisticsLiteral and figurative languageMeaning (existential)Eye trackingReading (process)Test (biology)Cognitive psychologyComputer scienceArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Abstract Irony comprehension requires going beyond literal meaning of words and is challenging for children. In this pre-registered study, we investigated how teaching metapragmatic knowledge in classrooms impacts written irony comprehension in 10-year-old Finnish-speaking children ( n = 41, 21 girls) compared to a control group ( n = 34, 13 girls). At pre-test, children read ironic and literal sentences embedded in stories while their eye movements were recorded. Next, the training group was taught about irony, and the control group was taught about reading comprehension. At post-test, the reading task and eye-tracking were repeated. Irony comprehension improved after metapragmatic training on irony, suggesting that metapragmatic knowledge serves an important role in irony development. However, the eye movement data suggested that training did not change the strategy children used to resolve the ironic meaning. The results highlight the potential of metapragmatic training and have implications for theories of irony comprehension.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.686
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.007
GPT teacher head0.285
Teacher spread0.278 · 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
Published2025
Admission routes1
Has abstractyes

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