Impact of Welfare Benefit Sanctioning on Food Insecurity: a Dynamic Cross-Area Study of Food Bank Usage in the UK
Bibliographic record
Abstract
Abstract Since 2009, the UK has witnessed marked increases in the rate of sanctions applied to unemployment insurance claimants, as part of a wider agenda of austerity and welfare reform. In 2013, over one million sanctions were applied, stopping benefit payments for a minimum of four weeks and potentially leaving people facing economic hardship and driving them to use food banks. Here we explore whether sanctioning is associated with food bank use by linking data from The Trussell Trust Foodbank Network with records on sanctioning rates across 259 local authorities in the UK. After accounting for local authority differences and time trends, the rate of adults fed by food banks rose by an additional 3.36 adults per 100,000 (95% CI: 1.71 to 5.01) as the rate of sanctioning increased by 10 per 100,000 adults. The availability of food distribution sites affected how tightly sanctioning and food bank usage were associated (p< 0.001); in areas with few distribution sites, rising sanctions led to smaller increases in food bank usage. In conclusion, sanctioning is closely linked with rising food bank usage, but the impact of sanctioning on household food insecurity is not fully reflected in available data.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".