A review of Chinese food safety strategies implemented after several food safety incidents involving export of Chinese aquatic products
Bibliographic record
Abstract
Purpose The rapid transition from a command to market‐based economy in China has required the development of a food safety system for aquatic products where one did not previously exist. The pace of change has meant that food safety systems have struggled to keep up. In 2007 food safety incidents damaged the reputation of aquatic products in export markets. The Chinese Government has moved quickly to strengthen the safety regime for aquatic products. The purpose of this paper is to assess these initiatives in the context of their potential to regain international acceptance of Chinese aquatic products. Design/methodology/approach A regulatory assessment approach is used. Findings The findings are that increased government oversight alone is not likely to lead to a fully effective food safety system for aquatic products. The development of private sector‐based incentives to encourage investment in food safety is an essential co‐requisite to increased government oversight if China's access to international markets is to be assured. Originality/value The value of this study lies in the light it sheds on the efforts of a major player in the international market for aquatic products to improve the efficacy of its food safety system. China's regulatory regimes are often opaque, limiting the ability of those wishing to assess the advisability of importing food products from China.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".