What Factors Determine Membership to Farmer Groups in Uganda? Evidence from the Uganda Census of Agriculture 2008/9
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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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".