China’s Dairy Import Industry: An Economic Analysis of Influencing Trade Factors
Bibliographic record
Abstract
One of the most dependable trends in a country’s transformation from an undeveloped, to developing, and to developed country is a growing demand for dairy products and milk. As China has undergone an unprecedented transformation over the last few decades since Deng Xiaoping’s Open Door Policy, China has followed this trend of an increasing demand for dairy products. As with other industries in mainland China, the domestic dairy industry is progressing at an incredibly fast rate. Yet at the same time when China is building its own industry to meet the growing demand, trade liberalization by joining the World Trade Organization has brought intense competition from foreign milk producers such as New Zealand, Australia, and the United States. This thesis examines the factors that influence various facets of the Chinese dairy industry, including import and export trade, consumer demand, and domestic and international competition. In addition to a deep background assessment of the Chinese dairy industry and market, a Constant Market Share econometric model is utilized to assess the varying levels of influence that different factors have on the industry by using three different time periods as a model of assessment for the whole industry.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".