Scaling up research-for-development innovations in food and agricultural systems
Bibliographic record
Abstract
The last decade has seen a growing interest in scaling up innovations to realise wider benefits from development investments. While numerous proven technologies, products and models have been successfully piloted, scaling them up through expansion, adoption and replication has proved challenging, particularly in poor regions of the world. The low uptake of innovations is partially attributed to the design of technologies, in a manner that is not compatible with local farming practices. At the same time, proven innovations fail to generate large impacts at scale because implementing actors have not sufficiently understood or effectively engaged with the scaling process. This article shares lessons from the Canadian International Food Security Research Fund (CIFSRF) that supported applied research to develop, test and scale up promising food and nutrition security innovations. Key lessons include ensuring that innovations are embedded within local socio-ecological systems; engaging end users throughout the research process and enabling participatory decision-making; and considering the investment returns of innovations for end-users.
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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 | 0.093 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".