The impact of COVID-19 on alternative and local food systems and the potential for the sustainability transition: Insights from 13 countries
Bibliographic record
Abstract
The COVID-19 pandemic has been a major stress test for the agri-food system. While most research has analysed the impact of the pandemic on mainstream food systems, this article examines how alternative and local food systems (ALFS) in 13 countries responded in the first months of the crisis. Using primary and secondary data and combining the Multi-Level Perspective with social innovation approaches, we highlight the innovations and adaptations that emerged in ALFS, and how these changes have created or supported the sustainability transition in production and consumption systems. In particular, we show how the combination of social and technological innovation, greater citizen involvement, and the increased interest of policy-makers and retailers have enabled ALFS to extend their scope and engage new actors in more sustainable practices. Finally, we make recommendations concerning how to support ALFS' upscaling to embrace the opportunities arising from the crisis and strengthen the sustainability transition.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".