Transforming food systems: a gendered perspective on local agricultural innovation in Cuba
Bibliographic record
Abstract
Compared to many countries, Cuba has made significant progress in advancing women's rights and gender equity; however, disparities remain. In the country's rural communities and agricultural sector, women continue to face barriers to equal participation and recognition for the value of their work. This case study shares the story of gender equity efforts that have been conducted within the framework of a broader development project-the Project to Strengthen a System of Innovation in Local Agricultural Development (PIAL, for its initials in Spanish). PIAL began in 2001 as a participatory plant-breeding initiative aimed at increasing the genetic diversity of key crops such as maize and beans. Over the course of two decades, the project's goals expanded to include an emphasis on increasing women's participation. In the beginning, those efforts focused on including women in the participatory plant-breeding activities, which enabled them to prioritize traits they cared about such as grain texture, cooking speed, and taste in the selection process. Over time, the participatory nature of the PIAL methodology empowered women to identify and pursue capacity-building in other areas of local agricultural innovation. While PPB remained central to PIAL, women also chose to pursue opportunities in seed bank management, leadership training, and small-scale farm-based entrepreneurship. The results of the PIAL work on gender have included not just more inclusive plant breeding, but also important economic improvements for rural women as they have been able to diversify their livelihoods, and social change as they have gained confidence and recognition as leaders in their households, communities, and beyond.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".