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Record W4414845452 · doi:10.1075/ijchl.24011.sun

Understanding unintended and unsuccessful ironies among Chinese primary school children

2025· article· en· W4414845452 on OpenAlexaff
Ningzi Sun, Rong Yan, Samad Zare, Huichao Bi

Bibliographic record

VenueInternational Journal of Chinese Linguistics · 2025
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsIronyComprehensionUnintended consequencesLiteral and figurative languageSarcasmSocial statusSignificant difference

Abstract

fetched live from OpenAlex

Abstract Despite the numerous studies on irony understanding, much remains unknown about how children comprehend unintended and unsuccessful ironies and the role of interlocutors’ social status in the recognition of ironic intention. The present study aimed to address this gap through randomly selecting 269 Chinese children in grades 3 and 6, with an average age of 9 and 12 years, respectively. The results indicate that: (1) both graders performed significantly better in the comprehension of regular irony than that of unintended and unsuccessful ironies; (2) there was no significant difference between 9 and 12 year-old children in the comprehension of unintended and unsuccessful ironies; (3) significant interaction between age and interlocutors’ social status was found in the understanding of unintended and unsuccessful ironies. 9-year-olds demonstrated a better understanding when both irregular types of irony happened between peers, while 12-year-olds showed a better performance under the teacher and students context. The above findings have valuable implications for figurative language teaching and research.

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 score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.017
GPT teacher head0.316
Teacher spread0.299 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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