The Impacts of Technology Adoption on Smallholder Agricultural Productivity in Sub-Saharan Africa: A Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper is a review article on the impacts of technology adoption on agricultural productivity in smallholder agriculture in the sub-Saharan African region. The use of agricultural technologies determines how the increase in agricultural output impacts on poverty levels and environmental degradation. Experience and evidence from countries within and around the sub-Saharan African region indicate that returns to agricultural technology development could be very high and far reaching. The factors affecting technology adoption are assets, income, institutions, vulnerability, awareness, labour, and innovativeness by smallholder farmers. Technologies that require few assets, have a lower risk premium, and are less expensive have a higher chance of being adopted by smallholder farmers. There are certain traditional smallholder agricultural technologies in sub-Saharan Africa that also have their own merits. Some of these technologies are more efficient in their use of scarce production resources than modern technologies. Modern researchers should therefore seek to understand the rationale behind traditional smallholder farmer behaviour in technology use. This will make their future technological interventions in smallholder agriculture more effective.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it