Children's understanding and production of verbal irony in family conversations
Bibliographic record
Abstract
This study examined how children use and understand various forms of irony (sarcasm, hyperbole, understatement, and rhetorical questions) in the context of naturalistic positive and negative family conversations in the home. Instances of ironic language in conversations between mothers, fathers, and their two children (M(ages) = 6.33and4.39years) were recorded during six 90-min observations for each of 39 families. Children's responses to others' ironic utterances were coded for their understanding of meaning and conversational function. Mothers were especially likely to ask rhetorical questions and to use ironic language in conflictual contexts. In contrast, fathers used hyperbole and understatement as frequently as rhetorical questions, and employed ironic language in both positive and conflictual contexts. Children also showed evidence of a nascent ability to use ironic language, especially hyperbole and rhetorical questions. Family members used rhetorical questions and understatement proportionately more often in a negative interaction context. Finally, older siblings understood irony better than younger siblings, and both children's responses revealed some understanding of ironic language, particularly sarcasm and rhetorical questions. Overall, the results suggest that family conversations in the home may be one important context for the development of children's use and understanding of ironic language.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".