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Record W2023073599 · doi:10.1108/bfj-07-2014-0244

Trust in and through labelling – a systematic review and critique

2015· review· en· W2023073599 on OpenAlexaff
Emma Tonkin, Annabelle Wilson, John Coveney, Trevor Webb, Samantha B. Meyer

Bibliographic record

VenueBritish Food Journal · 2015
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsLabellingDistrustMarketingFood labellingEmpirical researchValue (mathematics)OriginalityBusinessFood productsConsumer behaviourPsychologyComputer scienceSocial psychologyFood science

Abstract

fetched live from OpenAlex

Purpose – Distrust of conventional food supply systems impacts consumer food choice. This in turn has implications for consumer nutrition outcomes and acceptance of expert advice regarding food and health. The research exploring consumer trust is found across a broad range of research streams, and is not cohesive in topic or approach. The purpose of this paper is to synthesise the disparate literature exploring the interaction between food labelling and consumer trust to determine what is known, and gaps in knowledge regarding food labelling and consumer trust. Design/methodology/approach – A systematic search of trust and food labelling literature was conducted, with study results synthesised and integrated. Studies were then critically analysed for the conceptualisation of the consumer, the label, and their interaction with a framework developed using social theories of trust. Findings – In total, 27 studies were identified. It was found that not only is the current literature predominantly atheoretical, but the conceptualisation of labelling has been limited. Research limitations/implications – Further empirical research is needed to enable a more comprehensive understanding of the role food labelling plays in influencing consumer trust in food systems. Originality/value – This research develops a conceptualisation of the dual roles food labelling may play in influencing consumer trust in food systems. It distinguishes between trust in food labelling itself, and the trust consumers develop in the food supply system through food labelling. The novel theoretical model and synthesis provide a foundation upon which future research may be conducted.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.419
Threshold uncertainty score0.434

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.037
GPT teacher head0.272
Teacher spread0.235 · 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 designSystematic review
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

Citations102
Published2015
Admission routes1
Has abstractyes

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