Bibliographic record
Abstract
This chapter examines the conceptual relations between irony and satire. Many forms of satire, usually seen as containing elements of judgment, play, laughter, and aggression, may be considered discourse-level irony (i.e., satire is more evident in stretches of discourse, rather than in single utterances). Burgers illustrates this important point, as well as how satire expresses implied criticism, through consideration of several instances of television comedy programs, literature, internet news, and political commentaries. Satire may be differently explained by several prominent theories of irony (e.g., Gricean, pretense, echoic mention), each of which reveals the discourse-level nature of satirical communication. Burgers’ chapter describes various experimental studies looking at the impact that satirical language has on people’s attitudes toward different topics. As is all cases of irony, whether satire is successful in communicating speakers’ beliefs depends on a variety of situational (e.g., the specific media) and personal (e.g., who is the speaker, the addressee, overhearers, and their particular prior beliefs about some topic) factors. Even though satire may be a global phenomenon, how it is specifically employed in different cultures, and for different personal and social reasons, is very much a topic for future research.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".