Agricultural biodiversity as a link between traditional food systems and contemporary development, social integrity and ecological health
Bibliographic record
Abstract
Traditional food systems offer a key link between the social and economic resilience of smallholder farmers and pastoralists and the sustainable food and nutrition security of global populations. This paper addresses issues related to socio-cultural diversity and the continuing complex engagement of traditional and modern communities with the plants and animals that sustain them. In light of some of the unhealthful consequences of the 'nutrition transition' to globalized modern diets, the authors define and propose a process for a more successful food system transition that balances agro-biodiversity and processed commodities to support diet diversity, health and social equity alongside sustainable economic growth. We review empirical research in support of practice and policy changes in agriculture, economic development and health domains as well as cross-sectoral and community-based innovation. High-value food crops within domestic and global value chains can be an entry point for smallholders' participation as contributors and beneficiaries of development, while sustainable small farms, as purveyors of environmental and public health services, diversify global options for long-term adaptation in the face of environmental uncertainty.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".