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Record W2097220562 · doi:10.1017/s1742170514000234

Understanding consumer choices for Ontario produce

2014· article· en· W2097220562 on OpenAlexaffabout
Steven Dukeshire, Oliver Masakure, Julio Mendoza, Bev Holmes, Nathan E. Murray

Bibliographic record

VenueRenewable Agriculture and Food Systems · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsMichael Smith Health Research BCUniversity of GuelphWilfrid Laurier UniversityDalhousie University
Fundersnot available
KeywordsSchema (genetic algorithms)MarketingPerceptionBusinessContext (archaeology)PsychologyAdvertisingGeography

Abstract

fetched live from OpenAlex

Abstract Research has demonstrated growing public interest in local food and that this interest is driven by a number of factors including supporting local farmers, reducing the distance food travels, sustaining the environment, and food safety and quality. However, there has been very little research relating factors to actual purchase behavior. This study begins to fill that gap by relating consumer beliefs and values toward local foods with activities that support local foods as well as the actual purchase behavior for 22 fresh produce items. Data were collected through an Ontario-wide, web-based survey that is part of a longitudinal panel regarding food issues. Results from 1879 completed surveys indicated consumers had positive perceptions of local food and felt responsible for buying local, but also experienced barriers when trying to do so. Positive perceptions toward local food and a greater sense of personal responsibility to buy local were positively associated and higher barriers negatively associated with the likelihood of engaging in activities related to supporting local food as well as buying fresh produce items that were produced in Ontario. Implications of these findings are discussed in the context that consumers seem to have an overall orientation or schema to buying local in general, rather than a highly specific schema directed to one or a few particular products.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.938

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 science0.0000.000
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 head0.050
GPT teacher head0.197
Teacher spread0.148 · 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 designNot applicable
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

Citations6
Published2014
Admission routes2
Has abstractyes

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