Multimodal Discourse of Corporate Public Service Advertisements: A Case Study of China UnionPay’s “The Fairy Tale of Poetry”
Bibliographic record
Abstract
Multimodal discourse is a crucial method for presenting information in a comprehensive and impactful manner. Public service advertisements, in particular, employ multimodal discourse to convey distinct economic and social significance. This study examines the qualities and effectiveness of corporate public service discourse by analyzing China UnionPay’s public service advertisement “The Fairy Tale of Poetry”. The video is initially annotated using Elan. The video discourse analysis has three components: ideational meaning, interpersonal meaning, and textual meaning, which are all derived from Visual Grammar. The study of text conversation is thereafter conducted via LancsBox and Wordless. Several suggestions are put up to enhance the communication effectiveness of public service advertisements: Regarding video discourse, it proposes developing a strong connection between color, focus, and content. This may be achieved by employing visual storytelling strategies, such as altering backdrops, employing complete and metonymic representation, employing a chronological narrative, and integrating texts and pictures. Within the realm of verbal communication, the deliberate selection of sentence length and structure, together with the frequent use of significant words and the incorporation of a dialect, work together to highlight the significance of core ideas and evoke emotional resonance.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".