Bibliographic record
Abstract
This chapter examines the possibility that hyperbole and understatement are distinct notions and not necessarily under the superordinate concept of irony. Hyperbole relates to exaggeration or overstatement, while understatements are viewed as scalar shifts that are quite the opposite of hyperbole (i.e., presenting something as less significant than it is). She examines empirical evidence on the discourse goals associated with irony, hyperbole, and understatement to suggest that irony is frequently a part of hyperbole and understatement (e.g., to achieve the goals of contrast, expectations, and indeterminacy), but can exist on its own (e.g., to achieve the goal of an ironic attitude through evaluation accounts, negative-attitude accounts, and dissociative-attitude accounts). Both hyperbole and understatement are also evaluative, but not necessarily in an ironic way. Most generally, understatement and hyperbole may share common mechanisms with irony, yet are still important rhetorical figures in their own right.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".