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Record W2500599916 · doi:10.4324/9780080477862-14

The lure of tea: history, traditions and attractions

2004· article· en· W2500599916 on OpenAlexaboutno aff
L.E. Joliffe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsTourismBusinessDestinationsDiversification (marketing strategy)Consumption (sociology)MarketingAdvertisingPromotion (chess)Tea gardenGeographyPolitical scienceSociologySocial sciencePolitics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.582
Threshold uncertainty score0.227

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.194
Teacher spread0.167 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2004
Admission routes1
Has abstractyes

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