Bibliographic record
Abstract
Pictorial irony is another example of nonverbal irony. This chapter addresses the need for experimental work on these topics in light of the view that irony is deeply tied to human cognition and not just language. Katz examines different ways of distinguishing between irony and sarcasm, particularly in terms of “vector space theory,” which suggests that sarcasm is more aggressive, dark, and mocking than is irony. Additional empirical analyses note important distinctions in defining the notions of verbal vs. situational irony. Katz then applies his “constraint-satisfaction” model to create an open-ended list of visual features that likely signal the presence of irony or sarcasm in visual, including pictorial, displays. Katz argues that basic psychological processes involved in scene perception, which have deep evolutionary roots, are employed when people infer either sarcastic or ironic intents in pictures (including pictures with and without accompanying words). At the same time, similar psychological processes used in detecting pretense or echoic mention within language can also be adopted for understanding visual scenes as conveying sarcasm or irony. Expertise with some visual medium, such as painting, may enhance people’s abilities to readily interpret these as expressing irony in different ways.
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 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 | 0.000 | 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 | 0.001 | 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".