Analysis on the Characteristics of Tourism Flow of Chinese Independent Tourists in Vietnam
Bibliographic record
Abstract
Background/Objectives: As Vietnam’s largest tourists-generating country, China is an important tourism market for Vietnam.Methods/Statistical analysis: Through data mining and classification of online travel notes for Chinese independent tourists to Vietnam, this study analyzes the characteristics of tourism nodes of Chinese independent tourists in Vietnam by using the social network analysis method.Findings: The results showed that Ha Noi, Ho Chi Minh, Da Nang, Nha Trang, Da Lat were the main tourist destinations for Chinese independent tourists in Vietnam. Cities such as Ha Noi, Ho Chi Minh, Da Nang, Lao Cai, NhaTrang, Dalat have strong degree centrality that have strong aggregation and radiation ability to other tourism nodes. The closeness centrality of Ha Noi, Ho Chi Minh, Da Nang, Lao Cai, Nha Trang, Da Lat is higher that reflets the mobility and accessibility between these travel nodes and other nodes is good. Ha Noi, Ho Chi Minh, Da Lat, Da Nang and Lao Cai have high betweenness centrality, and they have strong control over other tourism nodes. This study once again verifies the research value of online travel notes, and the results can provide scientific basis for developing the regional tourism, designing the tourism routes, improving the tourism service facility and planning the tourism traffic in Vietnam.Improvements/Applications: The source of tourism flow of Chinese independent tourists to Vietnam should be studied, providing a basis for Vietnam to develop China’s tourism market and make precise marketing.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".