Food Systems Innovation to Nurture Equity and Resilience Globally (Food SINERGY): insights from the Food SINERGY network
Bibliographic record
Abstract
The international collaboration network Food Systems Innovation to Nurture Equity and Resilience Globally (Food SINERGY) unites food system experts concerned with the confluence of environmental, geopolitical, economic, and public health stressors that weaken food systems and increase inequalities. In March 2023, Food SINERGY participants from universities, research institutes, food policy advocacy groups, Indigenous networks, farmers’ associations, consumer organizations, social enterprises, and non-governmental organizations from around the world met in Mont Orford, Québec, for a forum to revisit food system structures across local-to-global scales and to identify key junctures for transformation. This article summarizes the network's discussions in the context of the existing literature. Key knowledge contributions include the importance of diversification throughout the food system for cultivating resilience; the value of food sovereignty in promoting equity across scales; the reconciliation between food sovereignty and equitable trade; the need for consonance between policy environments at different scales to enable positive societal actions; the pioneering role of food system innovations that challenge conventional political and economic structures, with emphasis on agroecology; and the need for critical self-reflection around knowledge production and knowledge use to better serve equitable food systems. These discussion outcomes provide insights for actors seeking to transform food systems in support of equity and resilience.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".