Bibliographic record
Abstract
As an independent discipline, pragmatics was through out thirty years’ development. It is also a young discipline. As a medium emerging commonly in advertising language art, humor has attracted wide attention of many producers. Previous scholars analyzed more from the perspective of grammar, vocabulary, rhetoric, etc. But the research of advertising language humor is lacking from the aspects of pragmatic rules. Our collection of data is quite open. Any advertisement that can be transcribed in to written form is our interest. They are excerpted from magazines, advertisement, collecting books, newspapers, TV and radio commercials. As we only focus on language humor, situational humor produced by visual performance is not involved this thesis. The advertising language art of humor has been widely paid attention. Based on the existing theories of humor research, author of this paper used many kinds of pragmatic theories to analyze English advertising humor language, and including reference, deixis, anaphora, presupposition, speech act theory, the cooperative principle, conversational implicatures, and the politeness principle. It can not only provide reference for the research of this field for later scholars, but also provide theoretical guidance for the AD makers of using humor language to produce a good advertising effect.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".