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Record W1976155811 · doi:10.1080/03031853.2013.770953

Urban agriculture's enterprise potential: Exploring vegetable box schemes in Cape Town

2013· article· en· W1976155811 on OpenAlexaboutno aff
Amy E. Thom, Beatrice Conradie

Bibliographic record

VenueAgrekon · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsUrban agricultureAgricultureCapeFood securityOrder (exchange)Profit (economics)Context (archaeology)BusinessSustainable developmentQuarter (Canadian coin)Agricultural economicsMarketingEconomic growthEconomicsGeographyPolitical science

Abstract

fetched live from OpenAlex

Urban agriculture is potentially an important element of land reform and economic development programmes in South Africa. The social value of urban agriculture, such as improving food security, developing a sense of community and promoting ecological conservation, is well documented. But in order to effectively contribute to development agendas, urban agriculture must also present viable, sustainable economic opportunities. This study sets forth vegetable box schemes as a context-appropriate, economically feasible urban agriculture enterprise for which there is growing consumer demand. A survey of 354 subscribers to vegetable box schemes in Cape Town is analysed, finding these households source half of their fresh produce and a quarter of their total groceries from box schemes. The study explores dimensions of consumer satisfaction and considers ways in which box schemes may be expanded. This paper also offers a brief comparison of different box scheme models in order to demonstrate that a development-oriented social enterprise model can compete with other for-profit models in the market.

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)0.001
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.175
Teacher spread0.162 · 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 designQualitative
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

Citations16
Published2013
Admission routes1
Has abstractyes

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