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Record W2474595336 · doi:10.21273/hortsci.48.2.200

Consumer Preferences for Local and Sustainable Plant Production Characteristics

2013· article· en· W2474595336 on OpenAlexaboutno aff
Bridget K. Behe, Benjamin L. Campbell, Charles R. Hall, Hayk Khachatryan, Jennifer H. Dennis, Chengyan Yue

Bibliographic record

VenueHortScience · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessPurchasingMarket segmentationProduction (economics)Product (mathematics)SustainabilityMarketingEconomics

Abstract

fetched live from OpenAlex

Some consumers are becoming more interested in and purchasing products that are locally grown and/or ecologically friendly. Market segmentation and product targeting are efficient methods to allocate a firm’s scarce marketing resources to supply heterogeneous markets. This study’s objective was to identify consumer segments, focusing on their gardening purchases, to determine whether there were differences in consumer preferences for provenance and environmental attributes for transplant purchases. Using a consumer survey of U.S. and Canadian consumers, we found that participants who purchased different plant types had distinct preferences for varying environmental attributes and provenances. We profiled nine consumer segments, identifying their plant purchases and preferences for local and sustainably grown products and plant containers. Results provide plant producers and retailers with market segments that can be identified and targeted and provide a basis for customizable marketing communications to enhance profits.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.076
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.231
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations69
Published2013
Admission routes1
Has abstractyes

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