Rethinking Rural and Agricultural Development Through Market-Oriented Technologies in Africa
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".