Determinants of Farmer Participation in the Vertical Integration of the Rwandan Coffee Value Chain: Results from Huye District
Bibliographic record
Abstract
This paper presents results on socio-economic factors influencing decision to participate in cooperative and intensity of coffee in Huye District of Rwanda. The analysis uses primary data collected from Huye district with representative sample size of 230 comprised both non-members and members of cooperatives. The study used Probit regression model to test the status of decision to participate and Tobit regression was used to determine the factors influencing the intensity of coffee. The results generally show that gender, education level, farm size, off-farm income, non-access to credits and non-record keeping are all important factors explaining decision to participate. On the other hand, off-farm income, no-access to credit, farm size, experience, farm under other crops cultivation and farm contract agreements found to influence the intensity. The paper concludes by suggesting strategic policy targeting to build stronger farmer’ cooperatives. These should allow the farmers to have access on market, inputs, credit, farm contract, price stability and trainings. Thus improve coffee productivity in terms of quantity and quality in the study area.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".