What Factors Determine Membership to Farmer Groups in Uganda? Evidence from the Uganda Census of Agriculture 2008/9
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
Government of Uganda and its development partners are targeting farmer groups as the vehicle for agricultural development because of the potential role they could play in promoting value addition, market and credit access. However there is limited empirical evidence on what drives membership to these groups. Using the Uganda Census of Agriculture 2008/9 data, this study reveals low levels of membership both at individual and household levels, with marked differences in regional participation. The key policy variables found to influence participation in farmer group included education attainment, distance to extension service and quality of road infrastructure. Thus, increasing membership to farmer groups requires government and its development partners to target more resources towards less educated farmers and those who live far from extension workers. The use of the local language in publicity materials is also important in ensuring participation among the illiterate and the less educated. Overall, there is a need for concerted efforts by all institutions supporting groups to ensure that existing groups have improved access to agricultural technologies and noticeable outcomes are achieved so as to attract more farmers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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