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Record W1974633215 · doi:10.4236/fns.2014.519203

Differences between Occasional Organic and Regular Organic Food Consumers in Germany

2014· article· en· W1974633215 on OpenAlexaff
Dörthe Krömker, Ellen Matthies

Bibliographic record

VenueFood and Nutrition Sciences · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsMcGill University
Fundersnot available
KeywordsTasteConsumption (sociology)MarketingBusinessVariety (cybernetics)Order (exchange)PsychologyAdvertisingFood scienceMathematicsStatisticsSociologyBiology

Abstract

fetched live from OpenAlex

It was the aim of this study to understand the differences between occasional organic consumers (OOC) and regular organic food consumers (ROC). A total of 571 consumers, interviewed directly after grocery shopping, were classified as conventional, occasional organic or regular organic consumers depending on the number of organically produced items bought. In order to gain encompassing insights on the differences between the ROC and OOC consumer groups, a large set of psychological and socio-demographic factors was studied. They differ with respect to general food choice motives with OOC placing significantly less importance on animal welfare, food security, environmental protection and more importance on caloric content, convenience and price compared to ROC; with respect to beliefs about the consequences of organic food consumption OOC expect greater expense, less choice, no increase in vitamins and no improvement in taste compared to ROC, and finally OOC show a less positive attitude, weaker social norms and lower intentions of buying organic food regularly in the future and give a lower importance in their lives to protection of the environment. OOC finally prefer different grocery stores and use a larger variety of stores than ROC.

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 score0.758
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.020
GPT teacher head0.205
Teacher spread0.185 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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