Measuring the Effect of Food Safety Incidents on China's Food Export: A Case Study on Aquatic Products
Bibliographic record
Abstract
In 2007 there were a number of food safety incidents with Chinese exports that drew media attention worldwide. There were widespread calls for tightening of regulatory barriers or outright bans on imports of Chinese foods including aquatic products. On June 28, 2007, America FDA announced a broader import detain of all farm-raised catfish, basa, shrimp, dace, and eel from China for residues from drugs such as the antimicrobials nitrofuran, malachite green, gentian violet, and flouroquinolones that are not approved in the United States. The EU, Japan, and Korea immediately followed to take action accordingly. As a result, consumer's confidence in Chinese foods suffered a serious decline and subsequently exports fell. The paper takes aquatic products as an example to analyze and comment on the effect of food incidents on China's food export from three respects including the exporting growths, the exporting markets structure and the exported species of aquatic products. The main problems on the China's aquatic product safety will also be discussed in this paper. Fortunately, China reacted quickly to the aquatic products safety incidents in order to minimize financial losses and to restore its reputation. The Chinese government has endeavoured to improve aquatic product quality and safety, issuing a series of new regulations on controlling aquatic product quality and safety. The conclusion is that incentives to invest in aquatic products safety in the private sector and safety training of workers all along supply chains and in the inspection service remain the major public health challenges. No food safety system can be completely effective. Food safety incidents will inevitably occur for both products of domestic origin and for imports. The key to maintaining trust in the safety of food is a quick and transparent response.The efficient solution to correcting it requires cooperation between the Chinese government and importing country governments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".