Bibliographic record
Abstract
According to the "2022-2027 China Internet Video Industry Market Depth Research and Investment Strategy Forecast Report" published by the China Research Institute of Industry, as of the end of June 2021, the size of China's Internet users broke one billion, reaching 1.011 billion people, an increase of 0.22 billion people compared to the end of December 2020, the massive size of Internet users to promote the development of China's online video industry. The size of the short video market will increase more quickly between 2020 and 2022, with a compound annual growth rate of about 44%. The market size will grow at a slower rate during 2023-2025, but will still maintain a CAGR of 16%. China's short video market is expected to reach nearly 600 billion yuan in 2025 [1]. More than a quarter of a day is spent watching short videos on mobile devices in China. Along with visuals and audio, short video has emerged as the "third language" of the mobile Internet. Short-form video has rapidly increased in the new Internet economy. Bilibili's future development has attracted much attention. With the development of the Internet economy and the increase in significant video websites, whether Bilibili can continue its competitive advantage and successfully achieve business transformation has become controversial. This research will analyse Bilibili's business model through a SWOT analysis and make feasible suggestions for its future development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".