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Record W2910810263 · doi:10.3968/10502

Research on the Development of Red Tourism Resources in Yan'an, China

2018· article· en· W2910810263 on OpenAlexvenueno aff
Jie Ren

Bibliographic record

VenueCross-cultural communication · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDiverse Aspects of Tourism Research
Canadian institutionsnot available
Fundersnot available
KeywordsTourismChinaEcotourismTourism geographySustainable developmentAlternative tourismGovernment (linguistics)Domestic tourismPovertyBusinessEconomic growthSustainable tourismEconomyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Objective: Red tourism is special to China. It is the combination of politics and tourism, education and tourism, culture and tourism. Background: Yan'an has become an is a very typical part of China’s red tourism destination. Method: The Yan'an city in the northwest China is selected as the study field in this paper, based on an understanding of contemporary red tourism, this article summarize the current situation of red tourism resources in the northwest China. We analyses the tourism resources from two individual respects: resources classification and brand position, based on the unique characteristics. Conclusion: The red culture inheritance, tourism promotes poverty alleviation, driving the development of red tourism in the northwest region of Yan'an city of red tourism sustainable development path, respectively from the balance the interests of relevant parties. We suggests strengthening cooperation with red tourist destination in northwest China, combining tourism routes and expanding the tourism market. Promoting the sharing of red tourism resources and tourists in northwest China, We will promote the development of red tourism. Application: The red tourism resources can make a significant contribution to the GDP of local government, it is critical to social well-being of citizens and the sustainable development of society, as well as to the formulation of related government policies.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.548
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.003
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.001
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 head0.136
GPT teacher head0.475
Teacher spread0.339 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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