Open on-Station System Experiments (OSEs) as innovation intermediaries to foster agroecological transitions: case studies from France
Bibliographic record
Abstract
Abstract The development of agroecology requires an ambitious, multi-level transformation of knowledge and innovation systems. The literature shows that intermediary actors and organizations play an important role in this transformation. In our article, we introduce the idea that agricultural research experiment stations can be considered as innovation intermediaries to foster agroecological transitions. Previous studies have shown how agricultural experiments are transformed by their inclusion in a multi-actor process, but they do not adequately explain how they contribute to the transition of agri-food systems. Our analysis focuses on nine case studies of on-station system experiments by France’s National Research Institute for Agriculture, Food and Environment. These on-station system experiments have the specificity to be managed by researchers interacting with non-academic stakeholders from their local areas. We have called these Open on-Station System Experiments. We documented them over five years, during which we collected information and data through observation, participation, eliciting activity, and cross-case analysis. We show how OSEs fulfil five knowledge and innovation intermediation functions that contribute, in practice, to the transition of agri-food systems: problem solving; production of transition visions through the design and experimentation of breakthrough agroecological innovations; production of operational and scientific knowledge responding to different users’ requirements; by generating operational and scientific knowledge based on Findable, Accessible, Interoperable, Reusable data; participation in networking between stakeholders and interactional learning about transition using experiments as boundary objects. Based on these findings, we show for the first time the conditions under which agricultural research experiment stations can fulfill the functions of innovation intermediaries and thus contribute to fostering agroecological transitions.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".