Nuru International: empowering farmers to fight extreme poverty
Bibliographic record
Abstract
Subject area Social Innovation and Entrepreneurship. Study level/applicability The case is suitable for graduate (MSc, MBA) and advanced undergraduate (BSc, BAs) students and applicable for course material focusing on social entrepreneurship, social ventures, strategic management, sustainable development and emerging markets. Case overview This case explores Nuru International, a non-profit enterprise established in 2008 with the mission to “end extreme poverty throughout the world”. Jake Harriman, the founder and CEO of NURU, together with his team are on the onset of diversifying crop offerings among Kenyan farmers in an attempt to alleviate challenges stemming from severe climatic changes and low-crop quality. As 2014 is the first year for Kenyan farmers to grow alternative crops, the Nuru team faces the challenging task of convincing farmers to embrace diversification. Additionally, as part of its proof of concept philosophy, Nuru is establishing operations in Ethiopia. There, Nuru has to identify best marketable crops and promote these among Ethiopian farmers while empowering and engaging local leaders in the process. Finally, the team is looking for financing opportunities for Nuru's entrepreneurial mission. Their funding opportunities come from the private markets, the philanthropic market and the impact investing space. They are carefully analyzing these options and looking for alternatives in capital markets. Pondering on Nuru's rewarding experience with KIVA, a Web-based lending platform, the team wonders if crowdfunding may be a viable option to finance Nuru's operations in Ethiopia. They are interested in equity crowdfunding but are not sure what might be the associated opportunities and risks. They, therefore, need to assess the merits of the practice and decide on how compelling it is for Nuru's expansion plans to Ethiopia. Expected learning outcomes The case aims to help students comprehend the role of hybrid organizational designs in meeting broad societal issues such as extreme poverty; evaluate collective impact initiatives in addressing strategic and behavioral changes for organizations operating in contexts of extreme poverty where partnerships are the key for success; assess diverse capital steams for social entrepreneurs and understand how these relate to the stages of evolution of a social venture; and elaborate on crowdfunding as a nascent source of capital for social enterprises. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".