Bibliographic record
Abstract
It is in the areas of tea services, tea attractions, tea tours and tea destinations that tea is clearly directly connected to tourism, as contemporary tourists seek out unique and authentic experiences related to the consumption and appreciation of the beverage called tea. For the tea industry, tourism related to tea encourages both the consumption of tea and the development of relationships with potential customers. Tourism clearly has the potential to enhance the brand image and marketing of tea-producing destinations such as Assam and Ooty in India. In Canada the tea industry has promoted the proper brewing and serving of tea in food service establishments serving tourists, and has extolled the health benefits of tea to a sympathetic public (Tea Council of Canada). In Sri Lanka tourism related to tea has been recognized as a potential strategy for the diversification of tea plantations and the encouragement of sustainable development in tea-producing regions (Tourism Concern, 2001). In India regional governments in tea-producing regions such as Assam are sponsoring tea festivals as both a way of nurturing relationships with potential customers and encouraging the development of tourism in their areas. These examples demonstrate the rich connections between tea histories, traditions and travel as well the relationship of tea to tourism that is explored and discussed in this chapter.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".