Irony, Prosody, and Social Impressions of Affective Stance
Bibliographic record
Abstract
In spoken discourse, understanding irony requires the apprehension of subtle cues, such as the speaker’s tone of voice (prosody), which often reveal the speaker’s affective stance toward the listener in the context of the utterance. To shed light on the interplay of linguistic content and prosody on impressions of spoken criticisms and compliments (both literal and ironic), 40 participants rated the friendliness of the speaker in three separate conditions of attentional focus (No focus, Prosody focus, and Content focus). When the linguistic content was positive (“You are such an awesome driver!”), the perceived critical or friendly stance of the speaker was influenced predominantly by prosody. However, when the linguistic content was negative (“You are such a lousy driver!”), the speaker was always perceived as less friendly, even for ironic compliments that were meant to be teasing (i.e., positive stance). Our results highlight important asymmetries in how listeners use prosody and attend to different speech-related channels to form impressions of interpersonal stance for ironic criticisms (e.g., sarcasm) versus ironic compliments (e.g., teasing).
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".