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Record W7008583552

CHANGES IN THE BEHAVIOR OF VEGETABLE CONSUMERS IN BUCHAREST AND THE NEIGHBORING AREAS CAUSED BY THE COVID-19 CRISIS

2022· article· en· W7008583552 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)HierarchyTasteConsumer behaviourQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Public opinion
DOInot available

Abstract

fetched live from OpenAlex

Romania ranks third in the hierarchy of vegetable growing countries in Europe. This study focuses on the determinants of the decision to buy fresh and canned vegetables and on the satisfaction of buyers on these purchases. The quantitative survey was used as a method of collecting information, and the investigation technique was used as an investigation technique, structured in the form of an opinion poll. The opinion poll applied is a questionnaire-based survey that provided information on the situation of vegetable consumers and the change in consumption behavior caused by the COVID -19 health crisis. Most respondents prefer buying directly from the market, buying between 1-3 kg per purchase. If in previous years taste and provenance were the basic criteria in the purchase of vegetables, the economic crisis caused by the health crisis COVID-19, brought the price in the first place, due to the decrease of buyers' incomes or other financial problems. The need for consumption/family, however, did not suffer, the quantities purchased being comparable to those of previous years, buyers turning to vegetables with a low degree of perishability. The study provides results on consumers' criteria for selecting vegetables and their hierarchical changes during the COVID-19 health crisis.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.022
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.295
GPT teacher head0.485
Teacher spread0.189 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueDOAJ (DOAJ: Directory of Open Access Journals)→Same topicCOVID-19 Pandemic Impacts→French-language works237,207→