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Record W2037538100 · doi:10.1108/00070701211213474

A review of Chinese food safety strategies implemented after several food safety incidents involving export of Chinese aquatic products

2012· review· en· W2037538100 on OpenAlexaff
Huanan Liu, William A. Kerr, Jill E. Hobbs

Bibliographic record

VenueBritish Food Journal · 2012
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFood safetyBusinessGovernment (linguistics)Context (archaeology)ChinaIncentiveMarketingEconomicsFood science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.037
GPT teacher head0.285
Teacher spread0.248 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations52
Published2012
Admission routes1
Has abstractyes

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