An Empirical Analysis of Influential Factors in International Tourism Income in Sichuan Province
Bibliographic record
Abstract
Sichuan Province is abundant in tourism resources, a big tourism province. Its tourism income occupies a relatively great rate in the total output value of local area. However, an analysis of the tourism income structure of Sichuan Province, it is found that whether in terms of the total output or the proportion it occupies, the international tourism income lags behind domestic tourism income. In the meanwhile, whether compared with such cosmopolis as Beijing and Shanghai or compared with Jiangsu and Shandong, the international tourism income of Sichuan Province occupies a small rate, which is out of line with the status of big tourism province of Sichuan Province. However, as a primary means for foreign exchange earning in Sichuan Province, the international tourism income has a significance that can not be ignored. Thus, it is necessary to analyze the influential factors that affect the international tourism income of Sichuan Province, take relevant measures to improve the international tourism condition in Sichuan Province, improve the international tourism income and make greater contributions to economic development of foreign exchange earning in Sichuan Province.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".