Views expressed are the author’s and do not reflect those of Agriculture and Agri-Food Canada or State Council DRC.
Bibliographic record
Abstract
Our main purpose is to discuss how prospects for China’s agri-food sector critically depend on the efficiency, commercial orientation, and responsiveness of China’s transportation system and intermediaries in the agri-food value chain. To provide context, we first examine the role that demographics play as a driver of growth and change in China’s agri-food sector and the economy at large. Two main findings stand out: (i) China’s population will become increasingly urban over the next 50 years; (ii) China’s economic “demographic dividend ” will expire in the late 2020s. These two demographic drivers motivate the discussion that follows. An increasingly urban population means that China’s society and economy must continue to evolve from one based on subsistence and local conditions to one that is market based and responsive. But a market economy also requires a wellfunctioning, efficient and responsive transportation and handling network. China’s transportation infrastructure has been improving steadily. Improvements in its road and vehicular infrastructure have been some of the most important catalysts of growth and competition. Its internal waterways, although improving, remain significantly underutilized. China’s railways have made noteworthy improvements in their passenger service but arguably been the least responsive mode to commercial incentives for freight transport. China has not been able to fully exploit its comparative advantage in high value, labour intensive perishable agri-food products due to its underdeveloped transportation and handling
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.184 | 0.070 |
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".