Understanding Irony in Literary Texts: A Cognitive Approach
Bibliographic record
Abstract
Cognitive approaches are particularly appealing when it comes to phenomena that can be adequately described only when their mental processing is taken into account. The phenomenon of irony is one of them. While there has been a plethora of semantic and structural analytic approaches in literary studies and linguistics that have focused on irony, the question is to what extent irony is already inherent in the texts themselves, to what extent it is only recognizable from contexts, and whether we recognize irony as such equally at all. In this article, we combine approaches from literary studies, cognitive psychology, and psycholinguistics in a corpus-based study. We ask whether irony is recognizable from certain textual features, such as genre or style. To determine this, we undertook a questionnaire study that indeed supports the conclusion that it is. It is the goal of the article to stimulate further contributions of this kind in order to explore the potential of a cognitive approach for the processing of irony in literary texts, providing a sound basis for future neuro-aesthetic investigations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".