A Review of Visual Metaphor Based on Visual Typologies and Verbalization Forms
Bibliographic record
Abstract
This study is a result of literature review of visual metaphors in the fields of linguistics and advertising. The collected fifty papers came from CNKI. It provided an overview of previous literature, in terms of multi-modal studies of visual metaphor in advertising, the typologies of visual rhetoric and the verbalization of visual metaphor. Based on the identified research gaps, this study proposed suggestions for future research to enrich the theoretical framework of visual metaphor. The review found that the study of visual metaphors remain insufficient in three aspects. Firstly, the relevant studies are mostly in the field of marketing, and lack of extension in linguistics. Secondly, most studies concerned about the cognitive effects of visual metaphor in advertising, but the cognitive processing form of visual metaphor was less focused. Furthermore, although some studies have proved that verbalization is necessary for the comprehension of visual metaphors, there is still no clear conclusion regarding the specific verbalization forms of different types of visual metaphors. Specifically, three syntactic structures have been hypothetically proposed for fusion-structured visual metaphors, as “A is like B, A is B, A with B”, but no empirical evidence suggests which is the most effective in conceptual representation of fusion-structured visual metaphors in advertising. Through the analysis of the differences between these syntactic structures, the author proposed that the verbalization form “A is B” should be the most effective in representing fusion-structured visual metaphors in the context of advertising for its basic metaphorical structure, easily comprehensible form and strong transformational effect.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".