Conceptualizing food justice in the food charity system in Prince George's County, Maryland, USA
Bibliographic record
Abstract
The well-documented flaws of the American food charity system include accessibility challenges, lack of food choice, and a dearth of nutritious food options. Creating a more just food charity system would require significant restructuring of existing networks and would emphasize agency and choice, allowing clients to select foods that are fresh, nutritious, and culturally appropriate and to do so at times and places that are convenient for them. Our study focuses on Prince George’s County (PGC), Maryland, and engages with people at each stage of the supply chain: urban growers as producers, food pantries as distributors, and food pantry clients as consumers. Our holistic analysis of the food charity system reveals the differences in perspectives about nutritional food access, equity, and convenience within it. Although confronting the root causes of food injustice is beyond the scope of this study, harm-reduction lessons from the food justice paradigm can meaningfully improve the existing system in the short term. Our recommendations highlight the ways that food pantries can alter their operations to reduce harm and move the food charity system closer to one that is just, accessible, and provides nutritious food options.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".