Bibliographic record
Abstract
Charitable food aid has become a first line of response for addressing rising rates of hunger in many high-income countries such as the United States (US), Canada, Australia and the United Kingdom (UK). This can be seen in the soaring numbers of food banks, alongside other charitable projects such as community kitchens, resourced through volunteer labour and food donations from corporate retailers. In the US and Canada, food banks have been an institutionalised response to food poverty for 35 years, and in the UK they can be traced back to the introduction of economic austerity measures implemented in response to the 2008 financial crisis (Lambie-Mumford, 2019). In the UK, where 8.4 million people live in food poverty, the largest national food bank provider, The Trussell Trust, has grown its network from 65 food banks in 2011 to more than 1,200 in 2019 (Sosenko et al, 2019). Australia’s largest food relief organisation, Foodbank, reports that during the 12 months leading up to 2019, the need for food relief increased by 22 per cent, with more than one in five people experiencing food insecurity. The organisation works with 2,400 charities to provide food relief but only 37 per cent reported that they were meeting the needs of those they assist (Foodbank, 2019). Critical voices in research, policy and advocacy argue that charitable food aid forms part of the retrenchment of the welfare state, allowing governments to devolve their responsibilities onto the charitable sector and community groups (Lambie-Mumford and Dowler, 2014; Barbour et al, 2016).
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.052 | 0.006 |
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".