Bibliographic record
Abstract
The opposition and unity of sound, meaning, and form constitute the three elements of language, that is, each language symbol contains three aspects: sound, meaning, and form. Due to the arbitrariness of language and certain contradiction caused by "less sound, fewer forms, and more meaning", the relationship between these elements in the language system is not one-to-one, leading to the phenomenon of polysemy and homonym in languages. Ambiguity is a common linguistic phenomenon that exists in both ancient and modern languages, both locally and across languages. It is a special relationship between language structure and meaning. Linguists believe that the phenomenon of ambiguity in the meaning of a word or sentence, or the existence of two or more interpretations for one expression, is called linguistic ambiguity. In this theoretical context, homonym is a type of lexical ambiguity widely used in modern advertising. The present study attempts to analyze the linguistic significance of homophones in advertisements through specific examples, so that people can better understand the wit, humor, and richness of languages, while paying attention to its impact on language development and adolescents. Through these measures, it can be expected that more benefits and less harm will be achieved in the long run.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".