Evolution Tendency of Chinese Cereals Trade and Its International Competitiveness
Bibliographic record
Abstract
Based on the data from UN COMTRADE DATABASE and WTO DATABASE,this paper analyzes the evolution tendency of Chinese cereals trade and its international competitiveness from 1995 to2012.The result shows that,Chinese cereals trade is net export-oriented from 1995 to 2008,and net import-oriented from 2009 to 2012 and net imports of cereals continues to increase.In recent years,Chinese cereals trade has also been in a net importer status,and the import amount has been on the increase.The commodity structures of Chinese cereal import and export both show remarkable periodical variation;and in recent years,the main imported cereals are wheat,barley and maize,and the main exported cereals are maize and rice.Chinese major cereals import markets are USA,Australia,Canada,Thailand,Vietnam and France,and export markets are East Asian countries/regions,including North Korea,South Korea,Japan,China Hong Kong SAR,Indonesia and Malaysia.Chinese cereals once had international competitiveness before 2008,but thereafter no longer internationally competitive.Chinese wheat and barley have not had international comparativeness all along,while maize and rice once had international comparativeness,but no longer internationally competitive in recent years.
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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 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".