Bibliographic record
Abstract
A vision for a more sustainable, just, and health-promoting food system comes from scholars, activist organizations, and communities alike. However, creating infrastructure and implementing policy that allows for the transition to a healthy, community-minded system comes with significant challenges and opposition, including a neoliberal policy legacy. Understanding the positive health impacts of improving the food system, thus, is crucial to making sense of and addressing the interconnected nature of food and health. The study of alternatives, and how these can be grounded and promoted in public policy, help challenge the notion that health issues in the food system are best solved through charity or technocratic fixes. To illustrate the role of such alternatives this paper draws on two case studies: the ScarbTO Mrkt Bucks initiative, a civil society group creating a system of subsidized vouchers for wider access to farmers markets at the community level, and the Coalition for Healthy School Food, a network of organizations advocating for federal investment in a universal cost-shared healthy school food program. So far, it is grassroots initiatives that have acknowledged the health issues that their communities face around accessing plentiful, diverse, and nutritious foods. These initiatives can connect local experiences with systemic and structural sources of inequity, leading to a more comprehensive means of change. Creating pathways to sustainable healthy food in public settings, I argue, is central to the wider, global transition to a healthier, more just food system.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.026 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".