Advancing food sovereignty through farmer-driven digital agroecology
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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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 | 0.029 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".