A Model to Promote Sustainable Social Change Based on the Scaling up of a High-Impact Technical Innovation
Bibliographic record
Abstract
The scaling up of the innovations concept has gained importance in recent years, underlining the importance of disseminating successful innovations on a large scale to increase their socio-economic impact, seeking to sustain broad rural development and promoting public policies that generate equal and sustainable results. However, few projects take advantage of the adoption of innovative technologies to promote social change. This paper reports on an intervention model that incorporates common elements, from the literature, of scaling up, and includes novel such as linking technological and social innovations, with an exit strategy to build sustainable processes and results. The model is based on a trans-disciplinary approach, and leadership, that involves local communities and stakeholders to establish synergies, to give coherence to the whole process, and to build an exit strategy involving territorial dynamics related to the innovation. The model includes a clear definition of the objects, and the process, that should be transformed, and the specific methodologies to involve local communities and stakeholders, to reach the final target user. The model was applied to develop the project, More Nutritious Potatoes, for scaling up new yellow potatoes varieties with improved agronomic and nutritional attributes. To implement the project, the research team, local communities, and stakeholders agreed on specific actions to improve food security and nutrition in a sustainable manner. The operational objectives were, the strengthening of capabilities for autonomy and governance in public policies, the entrepreneurship to build inclusive businesses, the valorization of agro-biodiversity, the acquisition of good food habits, and the promotion of social equity. The methodologies employed allowed building of social and scientific capabilities for Colombia.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".