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Record W2390552764

Study on the tourism market dynamic change of the Chinese Silk Road

2009· article· en· W2390552764 on OpenAlexaboutno aff
Han Chun-xian

Bibliographic record

VenueJournal of Shaanxi Normal University · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economic and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTourismMarket shareChinese marketBusinessEconomyMarket share analysisState (computer science)ChinaMarket economyGeographyEconomicsOrder (exchange)Market microstructureMarketingFinance
DOInot available

Abstract

fetched live from OpenAlex

The international tourism market changes of the Chinese Silk Road were analyzed by the market competitive state mode,the preference scale and market share.It showed that the tourism market of the Chinese Silk Road in 1995—2006 was in a deterioration state in the overall.The scenery market included Japan,US,Britain,France,Germany,Russia and Australia;the weak sparse scenery market was composed of South Korea and Singapore;and the strong sparse scenery market included the Philippines and Thailand.In the international tourism market of the Chinese Silk Road,Japan,US,Britain,France,Germany,Russia and Australia had the competitive advantages.Combined with the market share,the international tourism market of the Chinese Silk Road in 1995—2006 was divided into three categories: shrinking markets,such as Japan;the smooth expansion market,such as the United States,Britain,France,Germany and Russia;and the rapid expansion market,such as Canada,Australia,South Korea,Singapore,the Philippines and Thailand.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.021
GPT teacher head0.194
Teacher spread0.173 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2009
Admission routes1
Has abstractyes

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