Differential elements of a successful agricultural innovation scaling-up model
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
Worldwide, there is currently an increasing demand for an active connection between the generation of innovations and the achievement of their escalation. Between 2015 and 2018 the scaling up of three varieties of yellow potatoes was carried out in Colombia within the framework of the "More Nutritious Potatoes" project, which had results beyond the objectives and goals proposed in a period of 28 months. One of the results of the project was the design of a Scaling-up model of innovations that linked agriculture with nutrition. This article answers the question: Which were the elements of the scaling-up model that allowed the results obtained in the More Nutritious Potatoes Project? To respond, a set of reference criteria was constructed from the literature. These criteria were contrasted with the theoretical project scaling-up model and its subsequent implementation in the field, using focus groups as a methodology and the model design analysis and its execution by the leaders and the evaluator of the project. The project's Scaling-up Model (SM) was found to include all benchmarks, in addition to identify three key elements that made the results possible: (i) the characteristics of the innovation, (ii) the trans-disciplinary work and (iii) facilitating elements of the process. The results of this exercise complement the evaluated scaling-up model and become benchmarks in the design of innovation scaling-up processes.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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