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Record W4283327937 · doi:10.22434/ifamr2021.0145

Traceability issues of honey from the consumers’ perspective in Romania

2022· article· en· W4283327937 on OpenAlexfundno aff
Cristina Bianca Pocol, Peter Šedí­k, Alexandra-Ioana Glogovețan, Ioan Sebastian Brumă

Bibliographic record

VenueThe International Food and Agribusiness Management Review · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBee Products Chemical Analysis
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsBusinessPurchasingTraceabilityQuality (philosophy)MarketingBeekeepingPurchasing processDescriptive statisticsReputationEngineering

Abstract

fetched live from OpenAlex

The Romanian honey market is facing a problem related to traceability, especially when honey is produced in more than one country and its origin is indicated as a blend of EC and non-EC honeys. The increase of honey adulteration has consequences on both consumers and honey producers with considerable negative effects. The aim of the study was to identify the factors that influence honey purchasing behaviour and to evaluate consumers’ awareness related to honey adulteration in Romania among selected age segments. An online survey was conducted between 2020-2021 on a sample of 1,233 respondents. The questionnaire covered aspects related to purchasing behaviour and honey adulteration, complemented with socio-demographic questions. The data were evaluated using descriptive, non-parametric and multivariate statistics. The results showed that the most important factors considered during the purchasing process by Romanian honey consumers were health factor and country of origin followed by producer reputation and ecological aspect, while the least important were discounts, promotion and brand reputation. The older respondents are more aware of honey adulteration and know better that crystallisation is an indicator of quality. This study provides important information for policymakers and the whole beekeeping chain in Romania. Education in terms of honey authenticity and traceability will help consumers to choose local honey of high quality and to avoid adulterated products. This consumption and purchasing behaviour will discourage producers from honey counterfeiting.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.237
Teacher spread0.217 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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