Perceptions of Residents in Xinjiang, Urumqi towards Tourism Development through China’s Belt and Road Initiative
Bibliographic record
Abstract
China’s Belt and Road Initiative (BRI) clearly reads as an audacious vision for transforming the political and economic landscapes of Eurasia and Africa over the coming decades via a network of infrastructure partnerships across the energy, telecommunications, logistics, law, Information and Technology, transportation and tourism sectors. The BRI prioritises people-to-people connection. Various countries and cities will benefit from promoting cultural nationalism and local civic identities. The joint project of Belt and Road (B&R) embraces the trend towards a multi-polar world, economic globalization and cultural diversity for upholding global free trade, allocating many resources and deeply integrating marketers. The BRI is a remarkable example of the borderless nature of infrastructure development. This initiative aims to foster economic growth and investment along the ancient Silk Road trading route between Europe and the East. For instance, in Xinjiang, Urumqi, various resources promoting the culture of the area add value to the tourism industry. This study adopted a descriptive research design that encompasses a qualitative approach and addressed the residents’ perception and attitude towards tourism. In-depth interviews with residents were conducted. Recommendations were made for destination marketers and governmental practitioners on how to improve and facilitate tourism industry for Xinjiang, Urumqi.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".