Building an Agroecology Knowledge Network for Agrobiodiversity Conservation
Bibliographic record
Abstract
This paper describes the development of a transdisciplinary knowledge network dedicated to supporting agroecology knowledge exchange and capacity building that is particularly focused on the sustainable use and conservation of agrobiodiversity. The network—Fostering Effective Agroecology for Sustainable Transformation, or FEAST—includes nodes in Brazil, Cuba, Mexico, and Canada’s Northwest Territories and has been engaged in Participatory Action Research activities since 2015. This paper examines the development of the network over time, including a workshop held in 2019 in and around Curitiba, Brazil, and reflects on the outcomes of knowledge exchange activities. We discuss how the development of the FEAST network has informed participants’ local practice and their sense of belonging to a larger-scale, international movement for agroecology, agrobiodiversity conservation, and food system sustainability.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.002 | 0.002 |
| 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".