Surplus Food and the Rise of Charitable Food Provision
Bibliographic record
Abstract
Abstract Many wealthy but unequal countries have seen a significant expansion in systematized charitable food provision, usually in the form of food banks and food pantries. The use of food charity as a way to manage both food surplus and household food insecurity was pioneered in the United States at a time of cuts to cash-based welfare entitlements. It has expanded to Canada, Europe, Australia, and a growing number of middle-income countries, often in the wake of socio-ecological crisis including recession and pandemic, but also ideological shifts around effective and just solutions to poverty and inequality. While food charity is often presented as a “win-win” solution to food waste and hunger, it has been criticized from numerous perspectives that are explored in the article, including the argument that corporate-backed food charity in its currently expanding form masks structural causes and thus fails to resolve either problem, while offering a largely inadequate and undignified food offer to marginalized people. Alternative solutions include rights-based policies to ensure people’s access to basic needs, mutual aid in the face of systemic precarity, and movements for food sovereignty as means to address both ecological and social harms caused by existing food production and distribution systems.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".