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Record W2588667125 · doi:10.1186/s12889-017-4118-x

In the interest of food safety: a qualitative study investigating communication and trust between food regulators and food industry in the UK, Australia and New Zealand

2017· article· en· W2588667125 on OpenAlexaff
Samantha B. Meyer, Annabelle Wilson, Michael Calnan, Julie Henderson, John Coveney, Dean McCullum, Alex R. Pearce, Paul Ward, Trevor Webb

Bibliographic record

VenueBMC Public Health · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsUniversity of Waterloo
FundersAustralian Research Council
KeywordsVariety (cybernetics)Public relationsGovernment (linguistics)Food industryMarketingQualitative researchBusinessPublic healthMedicinePolitical scienceSociologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Food regulatory bodies play an important role in public health, and in reducing the costs of food borne illness that are absorbed by both industry and government. Regulation in the food industry involves a relationship between regulators and members of the industry, and it is imperative that these relationships are built on trust. Research has shown in a variety of contexts that businesses find the most success when there are high levels of trust between them and their key stakeholders. An evidence-based understanding of the barriers to communication and trust is imperative if we are to put forward recommendations for facilitating the (re)building of trusting and communicative relationships. METHODS: We present data from 72 interviews with regulators and industry representatives regarding their trust in and communication with one another. Interviews were conducted in the UK, New Zealand, and Australia in 2013. RESULTS: Data identify a variety of factors that shape the dynamic and complex relationships between regulators and industry, as well as barriers to communication and trust between the two parties. Novel in our approach is our emphasis on identifying solutions to these barriers from the voices of industry and regulators. CONCLUSIONS: We provide recommendations (e.g., development of industry advisory boards) to facilitate the (re)building of trusting and communicative relationships between the two parties.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.114
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.335
GPT teacher head0.395
Teacher spread0.060 · 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.

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

Citations13
Published2017
Admission routes1
Has abstractyes

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