Innovation in Business Models and Chainwide Learning for Market Inclusion of Smallholder Producers
Bibliographic record
Abstract
Th e potential for modern agribusiness to promote broad-based economic growth in developing and emerging market economies is significant. Food retailers, wholesalers, and processors have the potential to link smallholder producers to dynamic domestic and regional markets. They can contribute technical and managerial capacity building and investment. However,this potential is often underutilized, and in many countries, smallholder producers and small- and medium-scale enterprises are relegated to lower value markets. While agribusiness is not, nor should it be, driven primarily by a development imperative, the retail and processing stages rarely see the reach of their actions at the production level, including the implications for smallholder producers. Furthermore, many recent initiatives, often working with development partners, seldom result in any lasting economic empowerment or sustained market engagement of smallholder farmers or their organizations. In many countries, there is little policy dialogue between the private and public sectors and other key stakeholders, including farmers’ organizations and civil society. This paper explores the business challenge of and case for smallholder producers’ market inclusion. It looks at some of the evidences that demonstrates how business models and business processes can succeed in securing market inclusion. It includes producer- and buyer-driven business models, the development of new market intermediaries, changes in mainstream procurement policy, the development of domestic codes of good business practice, and global initiatives. Th is paper draws largely on business models and policy innovation from empirical research, case studies, and country-level multistakeholder chainwide learning events undertaken through a global program entitled Regoverning Markets program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".