On ironic puns in Portuguese authentic oral data: how does multiple meaning make irony work?
Bibliographic record
Abstract
In the present paper we focus on two Portuguese case studies (one in European Portuguese – EP and one in Brazilian Portuguese – BP) of the ironic oral discourse that result in verbal puns. In our analysis we postulate that studying multiple meanings (polysemy and homonymy) in puns can explain how irony functions on cognitive, linguistic and cultural levels given that (i) irony is a fundamental way of thinking about human experience (Gibbs & Colston, 2007, Gibbs, 2012); (ii) it is perspectivised and culturally grounded (Tobin & Israel, 2012, Dancygier & Sweetser, 2012, 2015); and (iii) linguistically explicit in verbal irony (Bryant, 2012, cf. Batoréo, 2016). The study is qualitative in character: the aim of the analysis is to exemplify cognitive and linguistic mechanisms culturally grounded that make irony work. In two case studies of authentic Portuguese discourse with ironic puns chosen out of larger corpora (cf. References) to be discussed in the present paper we shall argue that (i) polysemy and homonymy are cognitive and linguistic phenomena that trigger ironic puns; (ii) metonymy organised in metonymical chains or networks can be a complex cognitive mechanism that underlies polysemy; (iii) verbal puns are perspectivised and strongly culturally and historically grounded.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".