What Are You Really Saying? Associations between Shyness and Verbal Irony Comprehension
Bibliographic record
Abstract
Verbal irony exploits the ambiguity inherent in language by using the discrepancy between a speaker's intended meaning and the literal meaning of his or her words to achieve social goals. Irony provides a window into children's developing pragmatic competence. Yet, little research exists on individual differences that may disrupt this understanding. For example, verbal irony may challenge shy children, who tend to interpret ambiguous stimuli as being threatening and who have difficulty mentalizing in social contexts. We examined whether shyness is related to the interpretation of ironic statements. Ninety‐nine children (8–12 year olds) listened to stories wherein one character made either a literal or ironic criticism or a literal or ironic compliment. Children appraised the speaker's belief and communicative intention. Shyness was assessed using self‐report measures of social anxiety symptoms and shy negative affect. Shyness was not related to children's comprehension of the counterfactual nature of ironic statements. However, shyness was related to children's ratings of speaker meanness for ironic statements. Thus, although not related to the understanding that speakers intended to communicate their true beliefs, shyness was related to children's construal of the social meaning of irony. Such subtle differences in language interpretation may underlie some of the social difficulties facing shy children. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.013 |
| 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) | 0.003 | 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".