Transformative action towards regenerative food systems: A large-scale case study
Bibliographic record
Abstract
We urgently need to foster regenerative food systems that mutually reinforce human and ecological health. However, we have limited understanding of the action pathways that could encourage the emergence of such systems. Here we report on an extensive Three Horizons futures process, conducted with diverse participation from food system researchers and practitioners, to identify core domains of action for transforming the food system of Yorkshire, UK, towards a regenerative future. After establishing the contrast between the current degenerative and envisioned future regenerative food system, six core action domains were identified that require support to enable transformation: 1) enhancing supply chain connectivity and innovation to support diverse hybrid business ecosystems; 2) scaling environmentally beneficial and regenerative farming; 3) empowering citizens to reshape food demand; 4) providing trusted, accessible knowledge support for standards and incentives; 5) supporting schools and young people as drivers of long-term change; and 6) ensuring coordination and mutual support across domains. Our results highlight the importance of efforts to cohere synergic action, ambitious visioning, and addressing issues of power. Overall, our study sets an ambitious standard for co-developing action priorities to encourage regenerative futures.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".