Analysis of tourist source market potential in medium and small-sized cultural tourist cities: The traveling support strength perspective
Bibliographic record
Abstract
Tourist sources are playing significant roles in the research of tourism spatial structure and the development of tourist market,the analysis of tourist source market potential based on multiple indexes concluding sustaining capacity of social and economic,support capacity of tourism development,development capacity of tourism industry can be very useful for the urban tourism potential mining and urban tourism development decision. With correlation analysis and spatial analysis based on SPSS and ArcGIS techniques,we compared and analyzed the excursion and short-term tourism potential in the county region by using socio-economic indicators of 33 medium and small-sized cultural tourist cities. The conclusions show that the geographical location conditions which were caused by the spatial distribution of small and medium tourist cities are not the only causes of tourism potential,the traffic accessibility,the regional GDP,the non-agricultural population are also the key influential factors of the excursion and short-term tourism potential in the medium and small-sized cultural tourist cities. As the differences of tourist source market potential mining in the medium and small-sized cultural tourist cities,the scale efficiency of tourism development in different cities also show its striking diversity. At present,no matter in the coastal region,the central region,or the western parts of china,there are a large number of cities that can be further mining its tourist source market potential and became a fascinating place for its people to have a jaunt and relaxation trip in his free time. Although the traffic conditions is the important factors of deciding its tourist source market potential,the construction of traffic accessibility is not suitable for all the medium and small-sized cultural tourist cities of china. So small and medium tourist cities should positioning the limiting factor accurately with considering the present situation of theirs tourism development,chose the determinant or co-determinants among the outer tourism accessibility,the inner transport accessibility and the regional tourism cooperation accordingly,and search out the best way of excavating tourist source market potential and developing tourism.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".