The Analysis of Sustainable Business Model in Traditional Chinese Tea Industry
Bibliographic record
Abstract
Since ancient times, China has been a great tea country, the world’s largest tea planting area, and the world’s top tea production. Over the entire Chinese tea industry, there are countless large and small tea factories but few famous tea brands, seriously affecting the industry’s sustainable development. Thus, creating a famous tea brand is a huge challenge for Chinese tea. In addition, the world currently cultivates tea in many countries, such as Sri Lanka, India, Turkey, Kenya, etc. China is a large tea producer and is always an important economic crop. With the modern, fast-paced urban life, the new tea consumption style is more suitable for the domestic and international consumer market and meets the needs of the public. This paper explores to convert and upgrade the traditional Chinese tea industry’s business model by recognizing and analyzing the globally well-known brand Lipton Tea. Firstly, it is no longer limited to the traditional tea culture, and combining the tea culture with commercial activities to create a fashionable tea culture; secondly, to apply the tea to a wider range of consumer markets rather than limiting it to the tea-drinking market only; and then through the assistance of new media such as e-commerce and We media to promote the product, this series of upgrading from the internal to the external business model can improve the popularity of Chinese tea brands, the rapid development of the tea industry will also give China’s economic market to bring sustainable development of the new power.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".