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Record W1585956020 · doi:10.1300/j047v19n04_04

Predicting Consumers' Acceptability of Pesticide-Free Fresh Produce in Canada's Maritime Provinces

2007· article· en· W1585956020 on OpenAlexaffabout
Morteza Haghiri, Meaghan L. McNamara

Bibliographic record

VenueJournal of International Food & Agribusiness Marketing · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsMount Allison UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsWillingness to payPurchasingBusinessPrice premiumBachelorAgricultureAgricultural economicsSocioeconomic statusPesticideAgricultural scienceEconomicsMarketingGeographyEnvironmental healthPopulation

Abstract

fetched live from OpenAlex

ABSTRACT This study examines consumers' willingness to purchase pesticide-free fresh produce (PFFP) in Canada's Maritime Provinces. Households' decisions in purchasing organic foods are reflected in their willingness-to-pay (WTP) a premium for obtaining these types of products. WTP was modeled as a function of a series of explanatory variables including sociodemographic, socioeconomic, media, and public awareness about the impact of pesticide use on health and environment. Results suggest that Maritimes' consumers tend to pay the premium because they believe that the use of pesticide in conventional farming is life threatening. In addition, males and individuals with bachelor degrees are more willing to pay the premium, but those who visit farmers' markets on a regular basis are less likely to pay a premium for pesticide-free fresh fruit and vegetables. Finally, results show that media have no impact on consumers' decisions to purchase pesticide-free fresh produce.

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)0.004
metaresearch head score (Gemma)0.003
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 score0.227
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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 science0.0000.000
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 head0.025
GPT teacher head0.205
Teacher spread0.180 · 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

Citations14
Published2007
Admission routes2
Has abstractyes

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