Living Labs for Innovation in Agriculture: Where Does the Approach Go From Here?
Bibliographic record
Abstract
In this open letter, we examine the recent surge of international attention and implementation for living labs for innovation in agriculture, particularly those ultimately aiming to address complex agri-environmental issues or foster system-level transformations with agroecology. We recognize the first International Forum on Agroecosystem Living Labs as a key milestone in the conceptual development of the approach and in the exchange of implementation experiences from around the world. As the community prepares for the upcoming 2nd International Forum in October 2025, we take this opportunity to propose some key questions for discussion in the hopes that we may build on what has been accomplished so far, recognize remaining challenges, and chart a path forward together, as an international community.
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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 | 0.028 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.022 | 0.027 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.016 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".