Bibliographic record
Abstract
Abstract How persuasion is accomplished by speakers who use hyperbole and irony, in response to accusations of wrong-doing, was investigated in three experiments. Results confirmed a predicted dissociation – when accused speakers exaggerate denials (e.g., “I have never, ever stolen anything from this store”), they look relatively guilty compared to using no exaggeration (e.g., “I did not steal from this store”). But when accused speakers exaggerate ironic denials (e.g., “Oh sure, I have always, stolen everything from this store”), they are perceived as comparatively innocent relative to using no exaggeration. This dissociation is also not due to differences in hyperbolizing-toward-zero, versus hyperbolizing-toward-infinity, a difference which can affect pragmatic effects leveraged by hyperbole ( Colston & Keller, 1998 ). The results are interpreted as demonstrating the operation of psychological figurative comprehension and influence mechanisms both in parallel to and independent from similar pragmatic mechanisms found in some theories of linguistic pragmatics (e.g., Relevance Theory).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".