Inheritance Education of Wuzhou Liubao Tea to Promote Cultural Tourism
Bibliographic record
Abstract
Wuzhou Liubao Tea, a distinctive dark tea from Guangxi Province, China, represents more than a regional beverage—it embodies centuries of cultural knowledge, artisanal craftsmanship, and community identity. As modernization accelerates, the traditional practices surrounding Liubao Tea are increasingly at risk of being lost. This study investigates how inheritance education can be effectively implemented to preserve Liubao Tea culture while promoting cultural tourism in Wuzhou. The research was conducted in Liubao Town, Guangxi, using a qualitative ethnographic approach. Data were collected through field observations, semi-structured interviews with 40 informants, including educators, artisans, community members, and tourism professionals, as well as document analysis of educational materials and policy frameworks. Thematic analysis revealed five significant findings: the integration of tea culture into early and primary education, the development of vocational education pathways, strong school–enterprise collaboration, a direct educational impact on cultural tourism, and significant implementation challenges, including resource inequality and a lack of curriculum standardization. Results suggest that localized, experiential learning models foster intergenerational transmission and create authentic cultural experiences that enhance tourism. However, sustainability depends on unified educational frameworks, improved teacher training, and coordinated planning among the educational and tourism sectors. The study recommends expanding the model to other regions, enhancing educator support, and investigating the long-term effects on student identity and tourism 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".