A Corpus-Assisted Analysis of Conceptual Metaphors in K-Beauty Metaphoric Advertising
Bibliographic record
Abstract
Over the past few decades, the Conceptual Metaphor theory has attracted scholarly and practitioner attention in the construction of metaphoric advertising, which is driven by the claim that abstract concepts intended to be delivered are better communicated through a conceptual metaphor (Reddy 1979). Metaphoric advertising is a communicative mechanism that sends out intended messages while also provoking positive emotions and triggering attention from the recipient. In order to augment prior findings, this article aims to provide the metaphorical account of a conceptual mechanism delineated in K-Beauty metaphoric advertising by classifying their sub-types into structural, orientational, and ontological metaphors. To that end, this study compiled the K-Beauty metaphoric advertising corpora, which held over four thousand tokens of slogans and taglines extracted from beauty product advertisements generated in translated English, thus aiming to discuss primary instances of metaphorical mappings and conceptualizations, as well as their persuasive functions. Overall, the results evidenced that structural metaphors outperformed the other two sub-types, typifying systematic qualities and rich source domains. Of all the conceptual metaphor sub-categories, it was observable that the ENTITY and SUBSTANCE metaphors, which are a sub-type of ontological metaphors, were the most robust, signifying that ontological metaphors may adopt an imaging mechanism to transfer metaphorical mappings from a source domain to a target domain. The findings argue that particular metaphor choices in metaphoric advertising are closely linked to a cognitive mechanism triggered by cultural awareness pertaining to persuasion and promotion. Based on the current findings, implications and future research directions will also be discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".