A collective call to strengthen monitoring and evaluation efforts to support healthy and sustainable food systems: ‘The Accountability Pact’
Bibliographic record
Abstract
There is widespread agreement among experts that a fundamental reorientation of global, regional, national and local food systems is needed to achieve the UN Sustainable Development Goals Agenda and address the linked challenges of undernutrition, obesity and climate change described as the Global Syndemic. Recognising the urgency of this imperative, a wide range of global stakeholders - governments, civil society, academia, agri-food industry, business leaders and donors - convened at the September 2021 UN Food Systems Summit to coordinate numerous statements, commitments and declarations for action to transform food systems. As the dust settles, how will they be pieced together, how will governments and food corporations be held to account and by whom? New data, analytical methods and global coalitions have created an opportunity and a need for those working in food systems monitoring to scale up and connect their efforts in order to inform and strengthen accountability actions for food systems. To this end, we present - and encourage stakeholders to join or support - an Accountability Pact to catalyse an evidence-informed transformation of current food systems to promote human and ecological health and wellbeing, social equity and economic prosperity.
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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 | 0.407 | 0.408 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.011 | 0.033 |
| Scholarly communication | 0.035 | 0.044 |
| Open science | 0.006 | 0.032 |
| Research integrity | 0.025 | 0.048 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".