Enhancement of coffee quality in Rwanda: A stakeholder analysis of government policies
Bibliographic record
Abstract
Over the past two decades, Rwanda has positioned itself as a leading producer of specialty coffee. The shift away from ordinary coffee began in the early 2000s and was buoyed by international donors, NGOs and the government. They all supported the nascent specialty coffee industry by providing a combination of technical assistance and funding to invest in coffee washing stations. Coffee washing stations (CWS) are a pivotal piece of the value chain in Rwanda since it is where ordinary coffee undergoes a process that turn it into specialty coffee. The policy of shifting to specialty coffee has been significantly beneficial to Rwanda. However, there was a rush to build a large number of CWS throughout the country which has resulted in an over capacity of these plants and fierce competition among them for the purchase of cherry coffee from farmers. In an attempt to shore up the industry the Government implemented a zoning policy which effectively is a trade barrier to artificially maintain a high margin between the input price of cherry coffee and the sales price of coffee received by the CWS. This study uses a cost-benefit analysis to estimate the economic welfare loss to Rwanda of these policies. Over a ten year period the present value of the economic loss is estimated to be $73 million. An increased competition in the market for cherry coffee would raise the price of cherry coffee at the expense of the profits of CWS owners. If such a policy were implemented coffee growers could potentially receive up to 150% more from their sales of cherry coffee, or $45 million per year. These enhanced revenues would allow famers to finance the replanting of their coffee fees and maintain the sustainability of this sector.
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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 | 0.005 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".