Social Impressions of English Irony: A Comparison between L1 and L2 Perceptions
Bibliographic record
Abstract
Irony plays a critical role in social communication, yet its perceived politeness and appropriateness vary across linguistic and cultural backgrounds. This study investigated how Canadian L1 English speakers and advanced Chinese L2 English speakers evaluate ironic (sarcastic, teasing) versus literal statements in audiovisual conversations. Participants rated video-recorded utterances on politeness, appropriateness, and their own likelihood of using them. Stimuli varied by communicative intent (positive vs. negative) and delivery style (literal vs. ironic). Both groups judged literal and positive statements as more polite and appropriate than ironic and negative ones. However, only L1 speakers reported reduced willingness to use irony, suggesting a perception-usage mismatch among L2 users. Sarcastic remarks were judged less polite than literal negative ones, indicating that audiovisual cues may override the mitigating effect of positive surface language, contrary to the Tinge Hypothesis. These findings highlight the role of paralinguistic information in pragmatic evaluations and suggest that L2 speakers accommodate target norms in perception while retaining culturally grounded strategies in projected usage. Results contribute to intercultural pragmatics and L2 sociopragmatic development.
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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 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".