Bibliographic record
Abstract
China is a country with a long history. The earliest academic recognition of China's founding was in 221 B.C.E. when the Qin Dynasty was established. Therefore, China is proud of its long history and impressed with the current code of conduct because of its history and culture. The analysis of China's history and long-lasting celebrity culture will enable us to better understand the underlying logic of the spread of fame in the current live culture. This can also lead to a better understanding of the underlying logic of live fame transmission. This essay focuses on the relationship between modern live streaming and the history of Chinese celebrities in terms of the Chinese celebrity culture. Wang Zhuo in the Han Dynasty, Li Lili in modern times, and Li Jiaqi in modern times as representative figures to analyze how people from ancient times have turned their fame into wealth through means. And the ways and means of these three are strongly related to the modern means of live-stream and the strategies that influencers used. Therefore, this thesis aims to propose a new method of analyzing modern Chinese live-stream and tries to draw attention to the celebrity culture in China through these three examples. By analyzing the cultural characteristics of Chinese celebrities from the middle Ages to the present, we build a model that is applicable to today's Chinese live-streaming industry and influencers and deliver more effective ways to effectively increase the realization rate of fame.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".