Study on the influencing factors and measurement of high quality development level of tourism industry in Guizhou
Bibliographic record
Abstract
With the gradual improvement of the resilience and vitality of the tourism market, promoting the highquality development of the tourism industry with the new development concept has become an important fundamental issue for the sustainable growth of the regional green economy.The article measures and analyzes the level of high-quality development of Guizhou's tourism industry from 2012 to 2021 on the basis of constructing an evaluation index system for high-quality development of tourism, using methods such as entropy value method and gray correlation analysis.The results found that: the average value of the development index of Guizhou's tourism high-quality development subsystem is ranked in the order of GD, ED, ID, SD, OD and CD, the level of green development and effective development of Guizhou's tourism industry is higher, while the level of coordinated development of the tourism industry and the level of openness are insufficient; HQD, ID, GD, OD, SD and ED show a fluctuating upward trend, while CD is in a fluctuating downward state, and the tourism high-quality development system of Guizhou has gone through a fluctuating upward trend.Guizhou tourism high-quality development system has experienced three stages of evolution, namely, "stable rise, rapid rise and fluctuating rise", and the level of Guizhou tourism high-quality development and the development level of its various sub-systems have been affected by the New Crown Epidemic to varying degrees, with a greater impact on the level of open development of the tourism industry.GDP, per capita park green space area and tourism high-quality development index correlation is larger, while the total amount of SO2 emission and tourism high-quality development index correlation ranked at the bottom, tourism industry R & D funding is the most important factor affecting the level of high-quality development of Guizhou's tourism industry, and the total amount of SO2 emission has the smallest impact on it.On this basis, countermeasures for the high-quality development of Guizhou's tourism industry are proposed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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 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".