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Record W3213699246 · doi:10.5267/j.uscm.2021.10.002

The effects of laws and regulations on the implementation of food safety practices through supply chain integration and dynamic supply chain capabilities

2021· article· en· W3213699246 on OpenAlexvenueno aff
Binh An Nguyen

Bibliographic record

VenueUncertain Supply Chain Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSupply Chain Resilience and Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsFood safetySupply chainBusinessFood supplyFood packagingFood chainFood safety risk analysisSupply chain risk managementSafety assuranceIndustrial organizationSupply chain managementEnvironmental economicsMarketingRisk analysis (engineering)Service managementEconomicsAgricultural economicsFood science

Abstract

fetched live from OpenAlex

The objective of the article is to evaluate the role of laws and regulations on food safety in the implementation of food safety laws in Vietnam. Food safety regulations through business orientation, supply chain dynamic capabilities, and supply chain integration aim to ensure food safety and improve firm performance in Vietnam. Research data analysis is based on 389 food firms and the data are analyzed through Smart PLS 3.3.0 software. The results show the important role of food safety law in the implementation of food safety assurance of food enterprises. At the same time, supply chain dynamics and supply chain integration are also factors that have a statistically significant positive impact on the food safety performance of food enterprises in Vietnam.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.264
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations4
Published2021
Admission routes1
Has abstractyes

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