Emergency food supplies and food security in Wuhan and Nanjing, China, during the COVID‐19 pandemic: Evidence from a field survey
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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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 | 0.004 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".