A Computational Study of the Path of Cooperative Interaction of Small States in the Global Economic System – The Case of Malta
Bibliographic record
Abstract
Because of their inherent vulnerability, small States are in a relatively weak position in global economic relations.In order to survive and develop, regional cooperation is an important way for small countries to safeguard and promote their national interests.Taking Malta as an example, this paper utilizes the international trade data of some major countries along the "Belt and Road" from 2004 to 2018, and adopts the social network analysis method to construct the trade network of 37 countries in each year, and explores the formation of the trade network of the "Belt and Road" through the gravity model.The gravity model explores the major trade subgroups formed in the "Belt and Road" trade network and reveals the cooperation and interaction paths of small countries in the global economic system.Among them, Malta's position and role in the countries along the "Belt and Road", Malta still participated in global economic cooperation through Russia in 2004, and in 2018, in the more closely linked global economic network system, Malta strengthened its economic ties with China, which has a core degree of 0.897, further enhancing its own position in the global economic network.The economic scale and institutional differences between countries have different impacts on the trade of different products, and small countries have limited resources, so it is more important to formulate different economic development strategies according to different influencing factors and product types, and strengthen trade cooperation with neighboring countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".