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Record W2024680777 · doi:10.5539/jas.v5n7p91

A Matter of Trust: How Trust Influence Organic Consumption

2013· article· en· W2024680777 on OpenAlexvenueno aff
Sinne Smed, Laura Mørch Andersen, Niels Kærgård, Carsten Daugbjerg

Bibliographic record

VenueJournal of Agricultural Science · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)PurchasingOrganic productOrganic farmingOrganic productionWelfarePanel dataEconomicsBusinessEconometricsMarketingGeographyAgriculture

Abstract

fetched live from OpenAlex

This article shows that trust in the organic label as well as perceived positive health effects of consumption of organic products have positive causal effects on actual organic consumption. Furthermore perceived positive environmental effects and perceived better animal welfare related to organic production are found not to have no significant causual effect on actual behaviour, whereas concern for artificial additives and low price sensitivity have. Even when differences in time varying attitudes have been controlled for there is still a rather large heterogeneity in the organic purchasing behaviour. Part of this heterogeneity can be explained by differences in urbanisation or level of education, while income does not seem to have any effect when education has been controlled for. The data used is panel data for 830 households reporting actual purchases as well as stated preferences and attitudes in 2002 and again in 2007. The results point towards that the most efficient way of increasing organic consumption seems to be to continuously increasing the trust in the organic label and/or to document the positive health effects of organic food by e.g. focussing on measurable things such as a lower frequency of findings of pesticide residues in organic foods compared to conventional foods.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.883
Threshold uncertainty score0.999

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 communication0.0000.002
Open science0.0010.000
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 head0.009
GPT teacher head0.192
Teacher spread0.183 · 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.

Study designBench or experimental
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

Citations20
Published2013
Admission routes1
Has abstractyes

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