Empirical Analysis of the Relationship Between the Development of China's Tourism Industry and Economic Growth Based on VAR Model
Bibliographic record
Abstract
The relationship between tourism development and economic growth has been a hot topic in the field of tourism economy in recent years, and whether there is a long-term equilibrium relationship between tourism development variables and economic variables (usually GDP) is also a hot topic. By identifying the long-term equilibrium relationship between two variables, we can find the quantitative variation law (generally effect) of one variable with the other. Based on the vector autoregression of the time series data of China's tourism development from 2000 to 2019, it is found that there is a long-term equilibrium relationship between China's tourism foreign exchange income and domestic tourism gross income and their respective GDP, and the long-term effect is 99% respectively. Through the establishment of the VAR model for the development of China's tourism industry and economic growth, in the long run, they have a balanced relationship of mutual promotion, so as to further guide the development of China's tourism.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 source (direct Gemma or distilled Codex), 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".