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Strategy for Developing Chinese Tourism Service Industry From the Angle of Tourism Translators

2013· article· en· W1943733275 on OpenAlexvenueno aff
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Bibliographic record

VenueCanadian social science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicTourism, Volunteerism, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTourismService (business)ChinaBusinessOrder (exchange)Government (linguistics)MarketingQuality (philosophy)Service qualityConsciousnessPublic relationsCurriculumWork (physics)Political sciencePsychologyPedagogyEngineering

Abstract

fetched live from OpenAlex

In order to develop tourism service industry in China, tourism service should be taken into consideration based on human being and tangible service acting on tourists. Service quality of international tourism depends largely on Chinese tourism translators, so it is the key to improve the quality of tourism translators. From what aspects translators can be improved is an inevitable topic. The paper analyzes the features of Chinese tourism translators. Excellent tourism translators should possess the four good traits, that is, strong professional awareness, deepcultural consciousness, growing service awareness and highlevel of organization and coordination as well as strong team work ability. And then some strategies are put forward. In order to achieve the improvement of the above four facets, what requires in theory teaching in China is to adjust personnel training from curriculum setting and teaching method. In practical teaching what school and government need to do is to strengthen practical environment and to establish the platform of cooperative training together. These measures are of great significance of developing Chinese tourism service.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.286
Teacher spread0.259 · 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.

Study designQualitative
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

Citations2
Published2013
Admission routes1
Has abstractyes

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