Research on Sustainable Development Planning Strategies Under the Background of Rural Revitalization: Taking Yichang Cultural Tourism Town as an Example
Bibliographic record
Abstract
Under the background of "promoting rural revitalization in an all-round way," it is of great significance to study the sustainable development planning strategy of cultural tourism towns to realize rural revitalization. Taking the current situation of the development of cultural tourism towns in Yichang City as an example, this article deeply analyzes the development of cultural tourism towns in Yichang City. It also examines the phenomenon of blindly following the trend and homogenization in the development of rural cultural tourism towns in Yichang, including the hollowing out of town characteristics and the vulnerability of transition relying on a single tourism industry to market fluctuations. The article proposes a good job in the integrated development of cultural tourism towns and urban tourism resources to realize the outstanding characteristics of small towns, tap the potential of the physical industry of cultural tourism towns, do a good job in the cultivation and development of cultural and creative industries, and support the sustainable development of cultural tourism towns through planning strategy. The research results show that adopting a reasonable sustainable development planning strategy, focusing on the scientific planning and construction of cultural tourism towns, can effectively promote the coordinated development of rural and urban areas.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".