Fermented Food for Life: Stories of Inspiration, Struggle & Success
Bibliographic record
Abstract
The "Fermented Food for Life” project aims to improve food and nutrition security by increasing local production, distribution and consumption of health-promoting probiotic fermented yoghurt in Kenya, Tanzania, and Uganda, targeting to reach 250,000 consumers in the three countries by its end in June 2018. A pro-poor value chain-based business model was identified as a common approach to achieve project’s main objective. In Uganda, over 100 production units of probiotic yoghurt are up and running across the whole country reaching at least 60,000 regular consumers. The project supported also the establishment of 58 production units in Tanzania and 37 in Kenya, producing in total around 14,000 litres of probiotic yoghurt per week. In Tanzania and Uganda, 56% of production units are owned by women, and 68% of all people involved in probiotic yoghurt production and sales are female. The project has also provided a unique opportunity for employment of rural youth involved in the distribution of yoghurt. Partners in project’s implementation include Heifer International, Jomo Kenyatta University of Agriculture and Technology, University of Western Ontario and Yoba for Life Foundation. The project is undertaken with the financial support of Canada’s International Development Research Centre (IDRC), www.idrc.ca and the Government of Canada, provided through Global Affairs Canada (GAC), www.international.gc.ca\nCollected here are stories about the Fermented Food for Life project.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.005 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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".