Agriculture, health, and wealth convergence: bridging traditional food systems and modern agribusiness solutions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it