Advancing food sovereignty through farmer-driven digital agroecology
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
Agroecology, as a science, practice, and social movement, has been posed as a potential pathway to revitalize global food systems through a shift towards social and ecological justice. Complex and diversified agroecological systems vary widely globally and have been poorly characterized by traditional agronomic assessments that often focus narrowly on income and yield over other socioecological dimensions such as farmer and worker well-being, dietary diversity, environmental impacts and biodiversity conservation. In response, we propose an approach to the digital monitoring and assessment of agroecological practices that acknowledges and respects diverse contexts and improves power dynamics by centering on the agency and biocultural knowledge of diverse farmers and communities. We describe a community-university partnership designed to develop a farmer-driven, open-access, and open-source digital tool for agroecological monitoring and certification. The farmer-scientist research team aims to chart a course for researchers to investigate how trade-offs among productive, sociocultural, economic, and/or environmental indicators might be minimized to enhance overall system sustainability across diverse contexts globally while also providing tools of use to agroecological farmers and their organizations, who can then autonomously capture (some of) the benefits of the digital agricultural revolution without ceding data sovereignty.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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