Capturing Waste or Capturing Innovation? Comparing Self-Organising Potentials of Surplus Food Redistribution Initiatives to Prevent Food Waste
Bibliographic record
Abstract
The context for this article is the rapid international growth of (surplus) food redistribution initiatives. These are frequently reliant on networks of volunteer labour, often coordinated by digital means. Movements with these characteristics are increasingly viewed by researchers, policymakers and practitioners as cases of self-organisation. The article explores the nature and extent of self-organisation in food redistribution initiatives. Two contrasting UK initiatives were studied using ethnographic methods during a period of rapid expansion. The concept of self-organisation was operationalised using three dimensions—autonomy, expansion and governance. One initiative established food banks in close cooperation with corporate food actors. Its franchise charity model involved standardised safety protocols and significant centralised control. The other initiative deliberately pursued autonomy, rapid recruitment and de-centralised governance; nevertheless, collaboration with industry actors and a degree of centralised control became a (contested) part of the approach. We highlight the interplay of organisational agency and institutional structures affecting the self-organisation of surplus food redistribution, including ways in which movement dynamism can involve capture by dominant interests but also the seeds of transformative practices that challenge root causes of food waste, particularly food’s commodification. Our analysis provides a way to compare the potentials of food charity vs mutual aid in effecting systemic change.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".