Operationalizing the Recommendations from Nigeria 2021 Food Systems Dialogues : A Position of the Nutrition Society of Nigeria
Bibliographic record
Abstract
Food systems contribute to major global challenges including persistent poverty, competition over environmental resources, climate change and escalating hunger and malnutrition. The United Nations Food Systems Summit was convened to support governments to identify and implement actions that will transform national food systems toward achieving the Sustainable Development Goals. In Nigeria, more than 40 Dialogues involving over 4,000 multisectoral participants including academia, policymakers, the private sector, Non-Governmental agencies and the Nigerian Government were convened by the Nigerian government and other actors. A total of 79 recommendations from these dialogues were consolidated into six clusters to transform Nigeria's food system including; 1) Invest in food security and nutrition knowledge dissemination, skills development, and information management systems; 2) Build sustainable, responsive, and inclusive agricultural input supply and food production systems; 3) Develop value chains and market systems; 4) Increase demand for, and consumption of, adequate, nutritious, and healthy foods; 5) Promote peace-building initiatives, early warning systems, food marketing and regulation standards, and an enabling environment; and 6) Link research, innovation, and extension for a sustainable food system. The Nutrition Society of Nigeria explored the strategy to operationalize the 79 recommendations through a panel discussion and public lecture/engagement and her position includes the need for a national food systems dashboard and command centre; state governments support for food commodities of comparative advantage; filling critical gaps in building capacity for regulatory monitoring; improving on the existing national food-based dietary guidelines; integrating nutrition education into all efforts to transform food systems; active involvement of young people; leveraging the potential business/investment opportunities across the 79 recommendations to generate income while solving food systems challenges; re-positioning the academic/ research community in Nigeria to explore funding opportunities for food systems-related research and build consensus with other stakeholders to define priority research questions across the entire food system. The NSN is committed to supporting skills building around forming partnerships/collaborations, advocacy, and convening consultations to bring stakeholders together.
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 | 0.053 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 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".