Bibliographic record
Abstract
Dans un essai antérieur (« Why irony is pretence », in S. Nichols, dir., The Architecture of the Imagination, Oxford University Press, 2006) j’ai défendu une version de la théorie de l’ironie comme feintise — selon laquelle l’ironiste prétend adopter une perspective qui est en quelque sorte déficiente. J’ai aussi comparé cette version de la théorie de la feintise avec la théorie échoïque de Sperber et Wilson, en concluant que la théorie de la feintise était supérieure. Deirdre Wilson a répondu à cet article (« The pragmatics of verbal irony : echo or pretence ? » dans Lingua 116, 2006, 1722-1743). Dans le présent article, je réponds aux contre-arguments de Wilson. Je fournis aussi un contre-exemple à la théorie échoïque aidant à montrer que, contrairement à ce que pensent certains, la théorie échoïque et celle de la feintise ne sont pas équivalentes. Pour finir, je considère certaines conséquences, pour la théorie littéraire, de la conception selon laquelle l’ironie consiste à feindre avoir un point de vue déficient.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.034 |
| Scholarly communication | 0.007 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".