Viewpoint: Rigorous monitoring is necessary to guide food system transformation in the countdown to the 2030 global goals
Bibliographic record
Abstract
Food systems that support healthy diets in sustainable, resilient, just, and equitable ways can engender progress in eradicating poverty and malnutrition; protecting human rights; and restoring natural resources. Food system activities have contributed to great gains for humanity but have also led to significant challenges, including hunger, poor diet quality, inequity, and threats to nature. While it is recognized that food systems are central to multiple global commitments and goals, including the Sustainable Development Goals, current trajectories are not aligned to meet these objectives. As mounting crises further stress food systems, the consequences of inaction are clear. The goal of food system transformation is to generate a future where all people have access to healthy diets, which are produced in sustainable and resilient ways that restore nature and deliver just, equitable livelihoods. A rigorous, science-based monitoring framework can support evidence-based policymaking and the work of those who hold key actors accountable in this transformation process. Monitoring can illustrate current performance, facilitate comparisons across geographies and over time, and track progress. We propose a framework centered around five thematic areas related to (1) diets, nutrition, and health; (2) environment and climate; and (3) livelihoods, poverty, and equity; (4) governance; and (5) resilience and sustainability. We hope to call attention to the need to monitor food systems globally to inform decisions and support accountability for better governance of food systems as part of the transformation process. Transformation is possible in the next decade, but rigorous evidence is needed in the countdown to the 2030 SDG global goals.
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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 | 0.059 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.014 | 0.026 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.025 | 0.039 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".