Agriculture, health, and wealth convergence: bridging traditional food systems and modern agribusiness solutions
Bibliographic record
Abstract
The causes of many vexing challenges facing 21st-century society are at the nexus of systems involved in agriculture, health and wealth production, consumption, and distribution. Using food as a test bed, and on the basis of emerging roadmaps that set achievable objectives over a 1- to 3-year horizon, we introduce this special feature with convergence thinking and practice at its core. Specifically, we discuss academic papers structured around four themes: (1) evidence for a need for convergence and underlying mechanisms at the individual and societal levels; (2) strategy for mainstreaming convergence as a driver of business engagement and innovation; (3) convergence in policy and governance; (4) convergence in metrics and methods. Academic papers under each theme are accompanied by a roadmap paper reporting on the current status of concrete transformative convergence-building projects associated with that theme. We believe that the insights provided by these papers have the potential to enable all actors throughout society to singly and collectively work to build supply and demand for nutritious food, in both traditional and modern food systems, while placing the burdens of malnutrition and ill health on their core strategic agendas.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".