Envisioning the Future of European Food Systems: Approaches and Research Priorities After COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic unveiled the fragility of food sovereignty in cities and confirmed the close connection urban dwellers have with food. Although the pandemic was not responsible for a systemic failure, it suggested how citizens would accept and indeed support a transition toward more localized food production systems. As this attitudinal shift is aligned with the sustainability literature, this work aims to explore the tools and actions needed for a policy framework transformation that recognizes the multiple benefits of food systems, while considering local needs and circumstances. This perspective paper reviews the trends in production and consumption, and systematizes several impacts emerged across European food systems in response to the first wave of pandemic emergency, with the final aim of identifying challenges and future strategies for research and innovation toward the creation of resilient and sustainable city/region food systems. The proposal does not support a return to traditional small-scale economies that might not cope with the growing global population. It instead stands to reconstruct and upscale such connections using a “think globally act locally” mind-set, engaging local communities, and making existing and future citizen-led food system initiatives more sustainable. The work outlines a set of recommended actions for policy-makers: support innovative and localized food production, training and use of information and communication technology for food production and distribution; promote cross-pollination among city/region food systems; empower schools as agents of change in food provision and education about food systems; and support the development of assessment methodologies and the application of policy tools to ensure that the different sustainability dimensions of the food chain are considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.005 |
| 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".