Paths of convergence for agriculture, health, and wealth
Bibliographic record
Abstract
This special feature calls for forward thinking around paths of convergence for agriculture, health, and wealth. Such convergence aims for a richer integration of smallholder farmers into national and global agricultural and food systems, health systems, value chains, and markets. The articles identify analytical innovation, where disciplines intersect, and cross-sectoral action where single, linear, and siloed approaches have traditionally dominated. The issues addressed are framed by three main themes: (i) lessons related to agricultural and food market growth since the 1960s; (ii) experiences related to the integration of smallholder agriculture into national and global business agendas; and (iii) insights into convergence-building institutional design and policy, including a review of complexity science methods that can inform such processes. In this introductory article, we first discuss the perspectives generated for more impactful policy and action when these three themes converge. We then push thematic boundaries to elaborate a roadmap for a broader, solution-oriented, and transdisciplinary approach to science, policies, and actions. As the global urban population crosses the 50% mark, both smallholder and nonsmallholder agriculture are keys in forging rural-urban links, where both farm and nonfarm activities contribute to sustainable nutrition security. The roadmaps would harness the power of business to reduce hunger and poverty for millions of families, contribute to a better alignment between human biology and modern lifestyles, and stem the spread of noncommunicable chronic diseases.
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.
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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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".