Understanding Verbal Irony: Clues From Interpretation of Direct and Indirect Ironic Remarks
Bibliographic record
Abstract
Recently, irony researchers have emphasized that irony interpretation involves metarepresentational inferencing in order that the perceiver can determine whether the speaker's attitude is counterfactual to their statement. This research investigated whether the perception of irony also depends on the extent to which an ironic statement is suitably face-threatening in its particular context. Target statements were direct and indirect ironic remarks, and context conditions were modulated on 2 dimensions: context incongruity (strong vs. weak) and speaker–target relationship (close vs. distant). This study examined interpretations of ironic criticisms (Experiments 1 and 2) and ironic compliments (Experiment 3). Results showed evidence that suitable face-threat was important to a perception of irony: In some contexts (strongly incongruent context and close speaker–target relationships), irony was perceived more strongly for direct ironic remarks; and in other contexts (weakly incongruent context and distant speaker–target relationships), irony was perceived more strongly for indirect ironic remarks.
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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 | 0.013 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".