Global manifesto on forgotten foods
Bibliographic record
Abstract
This Manifesto on Forgotten Foods1 is the result of a broad and intensive consultation process carried out in Africa, Asia-Pacific, Europe and the Middle East (see Annex 1 for the roadmap). It was facilitated by GFAR as part of its Collective Actions to Empower Farmers at the Center of Innovation; led by a coalition of Regional Research Organizations and their partners, in particular, AARINENA, APAARI, FARA; and supported by CFF, and the Alliance of Bioversity International and CIAT. The content of this Manifesto is a landmark by presenting a coherent, multi-faceted but systemic, collective action-oriented proposal that covers research and innovation, and development (policy). The Manifesto aims to serve as a guide for the present and the future of forgotten foods. Its proponents call for a transformation of the agricultural research and innovation system through: change in research methodologies/paradigm; professional change; changes in the governance/ organization of development, research and innovation; changes in institutions; and changes in training/capacity building approaches and curricula. The Manifesto places smallholder farmers center stage, as producers and custodians of forgotten foods and related knowledge, agents of change and co-producers of (new) knowledge and practices. The Manifesto calls for concrete actions that contribute to achieve several of the Sustainable Development Goals of the United Nations, and to the ‘Right to Food’ and the ‘Right to Health’ embedded in the Universal Declaration of Human Rights. It is also meant to be an invaluable input for the United Nations Food Systems Summit later in 2021.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".