Theories of change: navigating diverse expert perceptions and preferences for global food system transformation
Bibliographic record
Abstract
Introduction Efforts are underway to transform food systems in light of their contributions to global challenges like climate change. However, food systems are highly complex, involve noteworthy place-based challenges, and there is often debate and disagreement among experts over appropriate technologies or interventions to prioritize. Tracking progress, and understanding these differences, is thus a critical need. Methods We surveyed food systems experts in eight countries about their preferences for 20 different food system transformation strategies and their sentiment regarding whether current initiatives are sufficient to meet 2030 goals for climate and biodiversity. Results Expert sentiment is overwhelmingly negative, and experts are concerned about multiple “transformation gaps,” including gaps in ambition, strategy, and implementation. Expert rankings for 20 strategies vary notably among countries and in ways that do not match those same experts’ rankings for the strength of the science behind each lever. Factor analysis reveals four distinct theories of change informing experts’ subjective biases: transformation via technical optimization, via smallholder support, via nature-positive solutions, and via supply chain enabling conditions. Discussion These findings provide insights for navigating the complexities of food system transformation and illustrate the influence on our strategies of preconceptions and biases in how we have come to understand the nature of the challenge.
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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 | 0.039 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".