Considering agro-ecosystems as ecological funds for collective design: New perspectives for environmental policy
Bibliographic record
Abstract
Enhancing agro-ecosystem sustainability raises difficult challenges for environmental policy: it requires both increasing knowledge on these complex systems to design effective solutions and coordinating stakeholders with diverging interests. However, most existing environmental policies consider ecosystems' desirable properties as given, leading ecosystem managers to favor "turnkey" solutions. How could public policy better support local collective initiatives aiming at reconciling agriculture and the environment? This paper presents an empirical case study from western France, in which a partnership between an agricultural cooperative and an ecological research center resulted in a collective design initiative. We conceptually model this initiative drawing upon recent design theories and Georgescu-Roegen's ‘fund-flow' model, defining ‘ecological funds' as the starting point of a collective design process. The results highlight the importance of developing policy instruments that can better support local innovation processes through greater democratization. Adopting a design approach to sustainable agricultural landscape management could be particularly fruitful in situations where collective action is necessary but where there is no common good recognized as such, and no existing community identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".