Bibliographic record
Abstract
The use of puns in advertisements is a common way for the advertisers to attract the consumers and persuade them to buy the advertised products. In the paper, many examples are presented in order to make the puns clear to the readers. First, the paper narrates the definition of a pun and then gives a detailed classification of English puns. Last, from the angle of language function the paper analyzes the typical functions of the puns in English advertisements. Key words: puns, English advertisements, vocative function, aesthetic function Resume: L’utilisation des mots a double sens dans la publicite anglaise est tres repandue. Les publicitaires ont souvent besoin de recourir a cette figure de rhetorique pour attirer les consommateurs et les persuader d’acheter leurs produits. A travers bon nombre d’exemples, cet essai precise d’abord la definition du mot a double sens, puis donne une interpretation detaillee sur la classification des mots a double sens, et analyse enfin, sous l’angle de la fonction langagiere, les fonctions principales du mot a double sens dans la publicite anglaise, a savoir celle d’impulsion et celle d’esthetique. Mots-Cles: mot a double sens, publicite anglaise, fonction d’impulsion, fonction d’esthetique.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".