Bridging two communities in farming system research: IFSA Europe Group and Farming System Design
Bibliographic record
Abstract
This study frames the conceptual and methodological bridges between the International Farming Systems Association (IFSA) Europe Group and the Farming System Design (FSD) community to strengthen farming system research (FSR). FSR has evolved from enhancing smallholder productivity to a holistic, systems-thinking approach emphasizing farm resilience, co-design, and farmer-driven innovation. On the one hand, the IFSA Europe Group, rooted in multi-level systemic transitions and transdisciplinary methods, fosters regional networks and links with agricultural knowledge systems. On the other hand, the FSD community prioritizes design-driven innovation through normative modeling and participatory approaches, aligning with agronomy associations. Despite shared foundations, their methods diverge, with IFSA broadening FSR boundaries empirically and FSD focusing on situated knowledge and modeling. Despite these complementary strengths—IFSA's empirical breadth and FSD's modeling expertise—both under-explore landscape-level dimensions. Building on their common ground in decision and design theories may enhance collective contributions to systemic transitions and sustainability in agri-food systems. This can be achieved by comparing their conceptual frameworks to reveal complementarities that could strengthen interdisciplinary cooperation. For example, a comprehensive review of relevant literature, including conference proceedings, is recommended to support this bridging effort and address existing knowledge gaps. In summary, this study calls for updated reflexive reviews and comprehensive synthesis of outputs, aiming to support cooperation in advancing systemic transitions and 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 | 0.098 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.017 | 0.018 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".