Teaching sarcasm: Evaluating metapragmatic training for typically developing children.
Bibliographic record
Abstract
Sarcasm is a common form of social language but one that can be challenging for children to understand. We investigated whether training can enhance children's ability to understand sarcasm. Participants were 111 five- to six- year-olds, assigned to either the training or control condition. In both conditions, children were first shown a series of puppet shows. Each puppet show ended with a sarcastic or literal remark. After each show children were asked about speaker belief, intent, and humour. Children's responses were used to estimate their pretraining sarcasm understanding. In the training condition, children were taught what sarcasm was and what cues to look for as they interacted with the researcher over a set of stories where characters made either sarcastic or literal remarks. In the control condition children read a nonsarcastic storybook with the researcher. Children's understanding of sarcasm was then reassessed using another series of puppet shows. For children who were not already proficient in sarcasm understanding, training was associated with more accurate speaker belief and intent responses and more accurate sarcasm detection. Training was not related to children's speaker humour responses. Thus, the findings of the present study provide evidence that aspects of children's sarcasm understanding can be enhanced through training. The training paradigm could be developed further to test its effectiveness in other populations who struggle with sarcasm understanding, and to help refine theories of irony development. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".