Bibliographic record
Abstract
Objective: Red tourism is special to China. It is the combination of politics and tourism, education and tourism, culture and tourism. Background: Yan'an has become an is a very typical part of China’s red tourism destination. Method: The Yan'an city in the northwest China is selected as the study field in this paper, based on an understanding of contemporary red tourism, this article summarize the current situation of red tourism resources in the northwest China. We analyses the tourism resources from two individual respects: resources classification and brand position, based on the unique characteristics. Conclusion: The red culture inheritance, tourism promotes poverty alleviation, driving the development of red tourism in the northwest region of Yan'an city of red tourism sustainable development path, respectively from the balance the interests of relevant parties. We suggests strengthening cooperation with red tourist destination in northwest China, combining tourism routes and expanding the tourism market. Promoting the sharing of red tourism resources and tourists in northwest China, We will promote the development of red tourism. Application: The red tourism resources can make a significant contribution to the GDP of local government, it is critical to social well-being of citizens and the sustainable development of society, as well as to the formulation of related government policies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".