Bibliographic record
Abstract
Francisco Yus proposed a verbal-visual (VV) model for analyzing media discourse based on Relevance Theory in 1997. This paper gives an introduction to Yus’ VV model and then applies the model to analyze three advertisements with the VV model’s four parameters of communication: exchange, message, intentionality, and efficiency. Yus’ VV model successfully captures all possible interpretive categories of the selected advertisements. The study finds that the first selected ad has one communication layer—spectator-oriented communication—and is intentionally conveyed non-verbally to readers. The second ad has two communication layers—character-oriented and spectator-oriented communication. The author intentionally conveys the message to readers both verbally and non-verbally. The last ad has one communication layer—spectator-oriented communication. The author intentionally uses non-verbal communication with the readers. For all three ads, the reader may have maximal or minimal interpretive efficiency based on individual differences. This study analyzes advertisements at different levels of communication from a cognitive perspective. Currently, few studies do so. And this study provides a reference for the analysis of media discourse under the framework of relevance theory.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".