What Does an Inventory of Recent Innovation Experiences Tell Us About Agricultural Innovation in Africa?
Bibliographic record
Abstract
Purpose: Within the context of the European-funded JOLISAA project (JOint Learning in and about Innovation Systems in African Agriculture), an inventory of agricultural innovation experiences was made in Benin, Kenya and South Africa. The objective was to assess multi-stakeholder agricultural innovation processes involving smallholders. Approach: Country-based teams used bibliographic searches, interviews with resource persons and field visits to identify cases. The inventory was developed iteratively according to a common analytical framework and guidelines inspired by the innovation system perspective. Findings and practical implications: The completed inventory includes 57 documented cases, covering a wide diversity of experiences, in terms of types, domains, scales and timelines of innovation. The inventory confirms the diversity of stakeholders involved in innovation, the diversity of innovation triggers and drivers, and the frequent occurrence of market-driven innovation. It also illustrates more original features: the typically long timeframes of innovation processes; the common occurrence of ‘innovation bundles’; and an often tight yet ambivalent relationship between innovation initiatives and externally funded projects. National teams faced several challenges during the inventory process, for example, in gaining a common understanding and making consistent use of key innovation-related concepts, and in accessing relevant information, as some case holders were reluctant to share their experience freely. Originality/value: The JOLISAA inventory contributes to illustrating that African agriculture is responding actively to the many challenges it faces. Documenting and sharing such a palpable dynamism may help to counter some of the pessimism and negative publicity that African agriculture usually attracts and to increase the motivation of many for making innovation happen across Africa.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".