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Record W1988797335 · doi:10.5539/jfr.v2n5p57

The Importance of Consumers’ Knowledge About Food Quality, Labeling and Safety in Food Choice

2013· article· en· W1988797335 on OpenAlexvenueno aff
Slavica Grujić, Radoslav Grujić, Đorđe Petrović, Jelena Gajić

Bibliographic record

VenueJournal of Food Research · 2013
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
Fundersnot available
KeywordsFood safetyQuality (philosophy)PurchasingProduct (mathematics)MarketingPreferenceFood qualitySet (abstract data type)Food choicePsychologyBusinessAdvertisingFood scienceMedicineMathematicsComputer science

Abstract

fetched live from OpenAlex

<p>With the aim to investigate existence of difference between responses of selected groups of young consumers representatives toward information associated with knowledge about food quality, labeling, safety and conditions of the product use, the results of survey were analyzed crossing the groups of consumers regarding: (1) education and (2) gender, with the other variables: (i) information related to food quality, labeling and food safety; (ii) information related to food safety; (iii) information associated with individual experience in food purchasing, preparing and consuming. The questionnaire offered answers with three grade of importance. Our research showed that groups of students formed on the basis of their education and gender, in our survey considered as representatives of young adults, had different interest for selected set of information included in the statements connected with food quality, safety and food choice. The results showed that there is a need for better informing and education of consumers about food quality and safety, labels and labeling, and how to use and interpret labels content. The results of research represent a qualitative set of information related to the food preference, which could be useful for creation and development guidelines for consumers’ better informing and education.</p>

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)0.005
metaresearch head score (Gemma)0.002
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 score0.113
Threshold uncertainty score0.529

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.129
GPT teacher head0.433
Teacher spread0.304 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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