Bibliographic record
Abstract
China's early industrialization created distortions. This paper identifies major distortions in the Chinese economy in the pre-reform era and brings agricultural distortions into perspective. Comparison is made of the reform experience in Chinese industry and agriculture. It suggests that with limited arable land, it is difficult to align Chinese agricultural production fully with its comparative advantage without also reforming China's grain policy. Reform has substantially freed up agricultural production but border distortions serve as one of a few remaining effective measures to ensure the grain self-sufficiency target. Unlike agricultural protection in rich countries, China's grain self-sufficiency policy ahs much weaker institutional underpinnings and is susceptible to the influence of interest groups. The patterns of Chinese agricultural trade explain its ambiguous positions in WTO agriculture negotiations. In terms of grain sectoral adjustment, a possible comprehensive China-Australia FTA is consistent with the multilateral process, while the China-ASEAN FTA is not. There is no evidence that the China-ASEAN FTA helps with the WTO agriculture negotiations, particularly when rice is excluded from the deal; but China-Australia FTA could generate competitive liberalization in grain trade, and thus help with the global agricultural liberalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".