Platform, Participation, and Power: How Dominant and Minority Stakeholders Shape Agricultural Innovation
Bibliographic record
Abstract
Within agricultural innovation systems (AIS), various stakeholder groups inevitably interpret ‘innovation’ from their own vantage point of privilege and power. In rural developing areas where small-scale and subsistence farming systems support livelihoods, dominant policy actors often focus heavily on participatory modernization and commercialization initiatives to enhance productivity, access, and quality. However, existing social hierarchies may undermine the potential of such initiatives to promote inclusive and sustainable farmer-driven innovation. Focusing on the chronically food insecure smallholder agricultural systems operating in Yatta Sub-county, Eastern Kenya, this paper explores how power dynamics between stakeholders can influence, and can be influenced by, participatory agricultural innovation initiatives. Findings suggest that there are often significant disparities in access to, and control over, platform resources between smallholder farmers and other stakeholder groups, resulting in large asymmetries. We discuss how these power dynamics may increase the risk of agricultural intervention, further marginalizing already disempowered groups and reinforcing power hierarchies to the detriment of smallholders. This study highlights the need for a deeper understanding of the institutional contexts that facilitate and maintain relationships of power within agricultural innovation systems, as well as the complexities associated with promoting transformational agricultural innovation.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".