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Record W4319787107 · doi:10.3389/fsufs.2023.970335

“Where everybody knows your name”: How regulars at farmers' markets differ from less-frequent shoppers

2023· article· en· W4319787107 on OpenAlexaffabout
Tracy Stobbe

Bibliographic record

VenueFrontiers in Sustainable Food Systems · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsPurchasingMarketingValue (mathematics)BusinessVariety (cybernetics)Grocery shoppingAdvertisingPsychologyMathematicsStatistics

Abstract

fetched live from OpenAlex

A survey of consumers at three farmers' markets (FMs) was done near Vancouver, British Columbia. The markets span urban and suburb locales, and the survey's 234 respondents were asked questions about shopping behavior, attitudes toward FMs, and demographic information. The focus of the analysis is on the differences between regulars and non-regulars to the market, where a regular is considered a shopper who shops weekly or bi-weekly. The results show that regulars spend more ($46.36 vs. 33.19 for non-regulars), are much more likely to expect higher prices compared to grocery stores than non-regulars, and buy more products (4.15 vs. 3.1). Regulars also value attributes of FMs differently: they value variety, organic products, and being locally-grown more highly. Organic purchasing behavior is also significantly different with regulars much more likely to say they “always” or “usually” buy organic products. As this is the first study to explicitly analyze regulars at FMs, suggested research directions and methods are offered to help guide future research.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.192
Teacher spread0.179 · 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

Citations2
Published2023
Admission routes2
Has abstractyes

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