The Research on the Influence of Transnational Relations in Video Social Media Upon Chinese Young Audiences’ Understanding of Multiculturalism
Bibliographic record
Abstract
This study discusses how the theme of transnational marriage in video social media influences young Chinese audiences' understanding of the concept of multiculturalism in the process of globalization and the rapid development of the Internet.To answer this question, this study adopts the method of thematic analysis.This study chose Bilibili, a very influential video software in China, as the platform, and selected four vloggers with the theme of transnational marriage, watched their videos and analyzed and classified their comments in the comment section.This study studies the audience's views on transnational marriage from two aspects of media and audience.Studies have shown that the gender of the audience, the gender of the media and the type of video all influence the audience's understanding of transnational marriage and multiculturalism.Videos on transnational marriage can not only deepen the audience's understanding and identification with other cultures, but also deepen the national pride and identity of their own culture.As an indispensable part in the process of globalization, multiculturalism has a significant impact on the construction of human society and the progress of thought.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".