Resilience in the Time of a Pandemic: Developing Public Policies for Ollas Comunes in Peru
Bibliographic record
Abstract
The coronavirus (Covid-19) pandemic has created economic, social, and food security crises in many countries throughout the world. Faced with growing hunger in Peru, and the government’s delayed and inadequate reaction, the most important response came from the citizens themselves, particularly the women, in the form of thousands of social care initiatives known as ollas comunes (literally ‘communal pots’, similar to soup kitchens, whereby local communities pool their resources to supply food for everyone in the neighbourhood). This article tells the parallel stories of the resurgence of these ollas comunes and the state-funded support initiatives, alongside the process followed by GRADE (Group for the Analysis of Development – Grupo de Análisis para el Desarrollo; a non-profit research centre founded in Peru) that enabled it to contribute to those institutions looking to improve access to food for the most vulnerable people. Both stories are underpinned by a common ability to adapt quickly, which is crucial for achieving objectives in uncertain and ever‑changing situations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".