Research on Quantitative Calculation of Tourism Development and Economic Benefit Enhancement by the Opening of High-Speed Railway in Ethnic Areas
Bibliographic record
Abstract
The construction and opening of high-speed railroads have brought new development opportunities to China's ethnic regions, which are economically backward but rich in tourism resources.From the perspective of the impact of high-speed rail on regional tourism, this paper briefly analyzes the homogenization effect and accessibility effect that the construction of high-speed rail brings to the corresponding region.Accordingly, it puts forward the relevant research hypotheses on the impact of high-speed rail on regional tourism and analyzes the current situation of tourism market development in China's A ethnic region.Under this premise, the model of high-speed railroad influence on tourism development level is designed and relevant research variables are selected.Based on the model, the empirical analysis of the impact of high-speed railroad on tourism in ethnic region A is launched.The study points out that the opening of high-speed railroad significantly promotes the total tourism income of ethnic region A at the 1% level, i.e., the opening of high-speed railroad has a positive positive effect on the tourism development of ethnic regions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".