Containing Hunger, Contesting Injustice? Exploring the Transnational Growth of Foodbanking- and Counter-responses- Before and During the COVID-19 Pandemic
Bibliographic record
Abstract
Abstract COVID-19 caused levels of household food insecurity to spike, but the precarity of so many people in wealthy countries is an outgrowth of decades of eroding public provisions and labour protections that once protected people from hunger, setting the stage for the virus’ unevenly-distributed harms. The prominence of corporate-sponsored foodbanking as a containment response to pandemic-aggravated food insecurity follows decades of replacing rights with charity. We review structural drivers of charity’s growth to prominence as a hunger solution in North America, and of its spread to countries including the UK. By highlighting pre-pandemic pressures shaping foodbanking, including charities’ efforts to retool themselves as health providers, we ask whether anti-hunger efforts during the pandemic serve to contain ongoing socioeconomic crises and the unjust living conditions they cause, or contest them through transformative pathways to a just food system. We suggest that pandemic-driven philanthropic and state funding flows have bolstered foodbanking and the food system logics that support it. By contextualising the complex and variegated politics of foodbanking in broader movements, from community food security to food sovereignty, we reframe simplistic narratives of charity and highlight the need for justice-oriented structural changes in wealth redistribution and food system organisation if we are to prevent the kinds of emergency-within-emergency that we witnessed as COVID-19 revealed the proximity of many to hunger.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".