Dismantling and rebuilding the food system after COVID-19: Ten principles for redistribution and regeneration
Bibliographic record
Abstract
The COVID-19 pandemic has claimed hundreds of thousands of lives and cost economies trillions of dollars. Yet state responses have done little to address the negative externalities of the corporate food regime, which has contributed to, and exacerbated, the impacts of the pandemic. In this paper, we build on calls from the grassroots for states to undertake a strategic dismantling of the corporate food regime through redistributive policies and actions across scales, financed through reparations by key actors in the corporate food regime. We present a strategic policy framework drawn from the food sovereignty movement, outlined here as the “5Ds of Redistribution”: Decolonization, Decarbonization, Diversification, Democratization, and Decommodification. We then consider what would need to occur post-redistribution to ensure that the corporate food regime does not re-emerge, and pose five guiding principles grounded in Indigenous food sover¬eignty to rebuild regenerative food systems, out¬lined here as the “5Rs of Regeneration”: Relation¬ality, Respect, Reciprocity, Responsibility, and Rights. Together these ten principles for redistri¬bution and regeneration provide a framework for food systems transformation after COVID-19.
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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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".