School food at home: Brazil’s national school food programme (PNAE) during the COVID-19 pandemic
Bibliographic record
Abstract
School closures during the COVID-19 pandemic have hindered students’ food access, particularly low-income students who rely on schools for their primary daily meals. School food programmes have adapted to pandemic conditions by providing school food at home (SF@H). We conceptually explore the changing geographies of school food during the pandemic by examining adaptations by Brazil’s national school food programme (PNAE) and then comparing it to regular school food provision. Our research is informed by 43 interviews with public officials and civil society representatives from all regions of Brazil, ranging from high-level technocrats to frontline responders engaged with school food. Rapid response through national school food policy allowed schools to provide food at home as a pandemic relief effort by creating novel alternative food geographies that keep schools at the heart of agri-food systems. SF@H provide local family farmers with an alternative commercialisation channel to those compromised because of social distancing measures. SF@H also provided students – and, for the first time, their families – with access to food during home-based learning. While this has been important, we find that even when the state provides SF@H as a pandemic relief measure, low-income families are subject to additional burdens that accentuate the inequalities previously ameliorated at schools.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".