The relationship between China-South Pacific island countries tourism and trade in the context of the Belt and Road Initiative
Bibliographic record
Abstract
In the context of the Belt & Road Initiative (BRI), China attaches great importance to tourism diplomacy with the South Pacific Island Countries (SPIC). The study of the relationship between tourism and trade is therefore of great significance for the further development of bilateral political and economic cooperation. This paper selected data on the inbound and outbound tourism, as well as import and export trade, between China and SPIC in the period 1998-2017, and used econometric methods to demonstrate the interconnections. The results show that there is a positive long-term equilibrium relationship between passenger flow and import and export trade of China and SPIC. Under the influence of policies, the interaction between inbound and outbound tourism and import and export trade is characterized by periodic fluctuations. China’s outbound tourism to SPIC plays a stronger role to import trade than to export trade. From the analysis of two sections, major events have a moderating effect on the proportion of SPIC tourists and trade dependence on SPIC, while both of the proportion of Chinese tourists and their trade dependence on China are on the rise, and these two have a pulling effect for each other.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".