E.U. and China Trends in Trade in Challenging Times
Bibliographic record
Abstract
The sudden and abrupt rise of COVID-19 became a challenge for the world economy. In this paper, we investigate the changes in a trend of mutual trade between the EU-15 countries and China during the demanding times of the COVID-19 crisis. We use monthly data for Chinese exports to the E.U. (2018:01–2020:05) and imports from the E.U. (2018:01–2020:07) relying on the data from the open-source TradeMap developed by the International Trade Centre UNCTAD/WTO (ITC). Overall, there is an obvious decline of 13–32 percent in worldwide trade as predicted by the WTO. This affected China as the main trading partner of electronic devices and medical supplies. The trade between the E.U. and China has decreased, but the major change in demand brought an alteration in commodities structures and the reorientation of Chinese export production. In the first five months of 2020, we witnessed the strong engagement of the Chinese economy in the production of goods newly in high demand—mainly articles strongly related to healthcare and medical equipment. Thus, we have observed that the Chinese were very flexible in changing the structure of their exports triggered by the COVID-19 crisis. This flexibility is worth further exploration, especially because the COVID-19 crisis is still not over and new data and changing results can be expected.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".