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Record W2021568189 · doi:10.5539/sar.v3n1p85

Rethinking Rural and Agricultural Development Through Market-Oriented Technologies in Africa

2014· article· en· W2021568189 on OpenAlexvenueno aff
B. O. Ovwigho

Bibliographic record

VenueSustainable Agriculture Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural marketingSustainabilityMarketingBusinessIncentiveAgricultural machineryAgricultural extensionAgricultureInvestment (military)Market systemAgricultural economicsIndustrial organizationMarketing managementEconomicsRelationship marketingMarket economy

Abstract

fetched live from OpenAlex

<p>The broad objective of the paper was to design a market oriented technology for agricultural and rural development in Africa. Marketing extension has been over looked in extension services delivery. Many of the agricultural technologies including the Top-Bottom, Feed-back, Farmer-Back to Farmer and Integrated Rural Development lacked in-built marketing components. The technology versus market component model was developed in this study. The model consists of the technical, market, and sustainability components as well as control mechanism. The theory of the technology and marketing component model states that, if the technical, marketing and sustainability components of a technology are properly designed implemented and controlled farmers will derive greater incentives from their investment. The marketing component should be specified in all agricultural technology after verifying the technical and sustainability components. Advisory services on marketing functions, role of cooperatives and organized markets in improving market incentives to the rural small and medium scale farmers were discussed. The model is recommended to research scientists and extension workers to adopt in a bid to improve the welfare of the rural farmers.</p>

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)0.003
metaresearch head score (Gemma)0.001
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 score0.838
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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