Differential elements of a successful agricultural innovation scaling-up model
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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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".