Analysis on the Inbound Tourist Market Structure and Development Strategies in XinJiang
Bibliographic record
Abstract
This paper analyzed the time,the space and consumption structure of inbound tourist market in Xinjiang from 1999 to 2008 based on geographic concentration index and year border concentration index according to Chinese Traveling Statistics Yearbook,China Statistical Yearbook and Xinjiang Statistical Yearbook as well as other data materials.It utilized the quantitative and qualitative method.The results showed as follows about the time,the space and consumption structure of inbound tourist market in Xinjiang.(1)It was still high for geographic concentration index of inbound tourist countries in Xinjiang.(2)Foreigners were the largest inbound tourist market in Xinjiang.The main inbound tourist market in Xinjiang was from Europe and Asia.The top 6 tourists were CIS,Japan,Taiwan,HongKong and Macao,America,Korea.CIS was the largest number among inbound tourists in Xinjiang.(3)Most of inbound tourists went to North in Xinjiang.Most of them flowed to Urumqi City,Ili Kazak Autonomous Prefecture,Turpan Administrative Offices,Kashgar Administrative Offices and Hotan Administrative Offices.(4)Most countries were relatively stable tourist markets for inbound tourism in Xinjiang,such as Malaysia,Japan,Philippines,Singapore,United Kingdom,Germany,France,Italy,Sweden,Switzerland,CIS,Canada,United States,Australia,New Zealand,Hong Kong,Macao and Taiwan.Annual big change was reflected among the countries which were Mongolia,India,Thailand,Indonesia,Korea and Spain.(5)Shopping,long distance transportation accounted for the proportion to be bigger and changed obviously.local transportation,visiting,accommodation,catering,post and communication services,entertainment and other services had a smaller proportion.The change was not obvious.The countermeasures for developing inboard tourist market were given.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".