Emergency food supplies and food security in Wuhan and Nanjing, China, during the COVID‐19 pandemic: Evidence from a field survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Motivation: Detailed empirical work on the impact of the COVID-19 pandemic on food security is scant. Local management of food security has received little attention. Purpose: This article describes emergency food policies in Wuhan and Nanjing, China during lockdown in 2020 and their implications for household food security in the two cities. Methods and approach: Policy documents and background data describe the emergency measures. Online surveys of residents of two Chinese cities were used to gauge household food security. Findings: Despite the determined efforts of provincial and city governments to ensure that food reached people who were locked down in Wuhan, or subject to restrictions on movement in Nanjing, households experienced some decline in food security. Most households found they could not access their preferred foods. But a minority of households did not get enough to eat.Government had contingency plans for the pandemic that ensured that most people had sufficient, if not preferred, food. But not all households were fully covered. Policy implications: A more resilient system of food distribution is needed, including a relatively closed and independent home delivery system. Grassroots organizations such as residential community committees, property management organizations, and spontaneous volunteer groups need to be brought into the management of emergency food provision.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it