Governance and resilience as entry points for transforming food systems in the countdown to 2030
Bibliographic record
Abstract
Due to complex interactions, changes in any one area of food systems are likely to impact—and possibly depend on—changes in other areas. Here we present the first annual monitoring update of the indicator framework proposed by the Food Systems Countdown Initiative, with new qualitative analysis elucidating interactions across indicators. Since 2000, we find that 20 of 42 indicators with time series have been trending in a desirable direction, indicating modest positive change. Qualitative expert elicitation assessed governance and resilience indicators to be most connected to other indicators across themes, highlighting entry points for action—particularly governance action. Literature review and country case studies add context to the assessed interactions across diets, environment, livelihoods, governance and resilience indicators, helping different actors understand and navigate food systems towards desirable change. This study presents the first annual update of the indicator framework developed by the Food Systems Countdown Initiative, published in Nature Food in 2023. Almost half of all indicators show some desirable trends. Governance and resilience indicators were revealed as the most connected across themes, constituting entry points for transformative change.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".