Scaling up research-for-development innovations in food and agricultural systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it