Are There Necessary Conditions for Inducing a Sense of Sarcastic Irony?
Bibliographic record
Abstract
This article investigates the contextual components utilized to convey sarcastic verbal irony, testing whether theoretical components deemed as necessary for creating a sense of irony are, in fact, necessary. A novel task was employed: Given a set of statements that out of context were not rated as sarcastic, participants were instructed to either generate discourse context that would make the statements sarcastic or meaningful (without further specification). In a series of studies, these generated contexts were shown to differ from one another along the dimensions presumed as necessary (failed expectation, pragmatic insincerity, negative tension, and presence of a victim) and along stylistic components (as indexed by the Linguistic Inquiry and Word Count program). However, none of these components were found to be necessary. Indeed, in each case, the items rated as highest in sarcasm were often at the lowest levels on the putative “necessary” characteristic. These data are taken as consistent with constraint satisfaction models of sarcasm processing in which various linguistic and extralinguistic information provide probabilistic (but not necessary) support for or against a sarcastic interpretation.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".