A Multimodal Discourse Analysis of "Common Destiny" in "The Belt and Road" Documentary on the Xuexi Qiangguo Platform
Bibliographic record
Abstract
Using visual grammar as the theoretical basis, the author makes an analysis of the use of images in the "Belt and Road" documentary "Common Destiny" and reveals that in the "Common Destiny" foreign publicity video the visual grammar analysis shows that the camera operator and the main characters in the "Common Destiny" foreign publicity video is realized via representational, interactive and compositional meanings of the images through the processing of vectors, contacts, viewpoints, distances, information values, framing, saliency values and borders. The analysis shows that the documentary " Common Destiny" mainly uses images of people, near and far viewpoints, social distance and enhancement and highlighting of information values to achieve three representational，interactive and compositional meanings of the video. The four colors of yellow, blue, red and black chosen in the video symbolize the positive and broad significance of the Belt and Road Initiative, and these elements of cultural context reflect China's offer of a Chinese solution for the development of the world and the building of a community of shared future for mankind.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".