Global Nutrition Report 2017: Nourishing the SDGs
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
A better nourished world is a better world. Yet the 2017 Global Nutrition Report shows that, despite the significant steps the world has taken towards improving nutrition and associated health burdens over recent decades, nutrition is still a large-scale and universal problem. Too many people are being left behind from the benefits of improved nutrition. Yet when we look at the wider context, the opportunity for change has never been greater. The Sustainable Development Goals (SDGs), adopted by 193 countries in 2015, offer a tremendous window of opportunity to reverse or stop these trends.The 2017 Global Nutrition Report shows there are five core areas of development that run through the SDGs which nutrition can contribute to, and in turn, benefit from:sustainable food productioninfrastructurehealth systemsequity and inclusionpeace and stability.Through these areas, the report finds that improving nutrition can have a powerful multiplier effect across the SDGs. Indeed, it indicates that it will be a challenge to achieve any SDG without addressing nutrition. The report shows that there is an exciting opportunity to achieving global nutrition targets while catalysing other development goals through 'double duty' actions, which tackle more than one form of malnutrition at once. Likewise, potential 'triple duty actions', which tackle malnutrition and other development challenges, could yield multiple benefits across the SDGs.If readers take away one message from this report, it should be that ending malnutrition in all its forms will catalyse improved outcomes across the Sustainable Development Goals (SDGs). The challenge is huge, but it is dwarfed by the opportunity.The 2016 Report was funded through the support of the Bill & Melinda Gates Foundation, the CGIAR Research Program on Agriculture for Nutrition & Health, the Children's Investment Fund Foundation, the European Commission, the Governments of Canada, Germany, and the Netherlands, Irish Aid, UK Department for International Development (DFID), US Agency for International Development (USAID), and 1,000 Days.The Report is delivered by an Independent Expert Group and guided at a strategic level by a Stakeholder Group, whose members also reviewed the Report. The International Food Policy Research Institute (IFPRI) oversees the production and dissemination of the Report, with the support of a virtual Secretariat. The American Journal of Clinical Nutrition managed the blind external review process for the Report, which was launched on June 14, 2016. Check our events page throughout the year for news on follow-up events.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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