Emerging Tourism between Pakistan and China: Tourism Opportunities via China-Pakistan Economic Corridor
Bibliographic record
Abstract
Background: The China-Pakistan Economic Corridor (CPEC) is a mega-project worth more than 54 billion US dollars, as a result of which bilateral relations between Pakistan and China reached new heights. The CPEC is designed to facilitate the establishment of links between Pakistan and the road network, railways and pipelines in conjunction with energy, industrial and other infrastructure projects to ensure the critical energy shortage necessary to enhance the economic growth in Pakistan.Objective: The main purpose of this article is to shed light on promotion of mutual understanding on China's initiative for the revival of the Silk Road and the benefits and challenges for the tourism industry which the CPEC can bring to the neighboring countries, especially Pakistan. A very new project will give us plenty of room to develop a number of innovative points greatly to improve the quality of services and the overall tourist experience in these new tourist destinations.Methodology: Qualitative research and analysis with the help of online research and data collection; the study of excellence in individual scenarios tourist sites, focusing on the aspects of service and policy will be useful to improve tourism on both sides via the Silk Road. Authors also collected data from tourist websites and recommend top rated tourist attractions on Silk Road from Khunjrab pass (border between china and Pakistan) to Gawadar, Pakistan. These tourists’ attraction are hints for tourists, travel agents and new researchers.
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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 | 0.000 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".