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Record W379677597 · doi:10.5539/sar.v5n1p86

Entrepreneurship on the Farm: Kentucky Grower Perceptions of Benefits and Barriers

2016· article· en· W379677597 on OpenAlexvenueno aff
Amy Camenisch, Sandra Bastin, Amanda Hege

Bibliographic record

VenueSustainable Agriculture Research · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)BusinessMarketingProduct (mathematics)Added valueMarket penetrationLegislationValue addedPopularityValue (mathematics)Order (exchange)Agricultural scienceEconomicsFinance

Abstract

fetched live from OpenAlex

The popularity of buying local and the resurgence of farmers markets has increased the need for farmer product diversification. In Kentucky, legislation was passed to allow farmers to produce value-added horticulture products from their homes. Following specific food-safe guidelines, homebased processors (HBP) and microprocessors (HBM) could sell pre-determined value-added products at their local farmers markets. This study administered an online survey to HBP and HBM participants in order to achieve the following objectives: 1) Determine the perceived success of farmer produced value-added food products, 2) Identify which support programs farmers are aware of or use, 3) Discover the primary perceived barriers to developing value-added food products, and 4) Ascertain what factors influence the development of a value-added food product business. Participants felt their value-added products were successful but many felt they struggled to bring their products to market. The primary barriers to developing value-added products were lack of time, funding, and legal knowledge. The primary barriers to using pre-existing program resources were not having enough time, being unaware of the services offered, and programs being held in locations too far away from their farm. The information gathered by this study can be used to determine the addressable farmer needs in product diversification. It can also assist programs in making their services more available and applicable to farm entrepreneurs.

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)0.001
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 score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.023
GPT teacher head0.251
Teacher spread0.228 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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