How to Enlarge China’s Trade with Middle & Small European Countries Such as Belgium
Bibliographic record
Abstract
In 2004 & 2005, China has been world No.3 import & export country, EU has become China’s No.1 trading partner. To further expand Sino-EU trade & turn China from a big trading into a high-tech, strong trading country, it is wise related China’s governmental & non-governmental organizations(such as trade unions & financial organizations) set up their global network, act as intermediaries, provide market information & guarantee to set up high-efficiency economy. Keywords: small European countries, governmental intermediary, NGO Resume: En 2004 et 2005, la Chine est devenue le troisieme pays d’importation et d’exportation du monde entier. L’Union europeenne est devenue le premier partenaire commercial de la Chine. Pour approfondir le commerce sino-UE et aussi pour transformer la Chine d’un grand pays de commerce en un grand pays fort en commerce avec des technologies de pointe, il est lucide d’encourager aux organisations gouvernementales et non-gouvernementales( telles que les unions de commerce et les organisations financieres) d’etablir leur reseaux globaux, en jouant le role d’intermediaires, en fournissant les informations et garanties du marche pour etablir une economie de haute rentabilite. Mots-cles: Petits pays europeens, intermediaire gouvernemental, NGO
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.005 |
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".