Governing for food security during the COVID-19 pandemic in Wuhan and Nanjing, China
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
The global COVID-19 pandemic has elicited a range of public health governance responses. One common result has been an associated disruption of food supply chains and growing urban food insecurity. Policy responses to this situation have not yet received sufficient research attention. This paper therefore focuses on the urban food security implications of China's zero-COVID public health measures and the response of central, provincial and municipal government to the governance challenge of ensuring a stable and sufficient food supply to urban consumers. During the COVID-19 outbreak in early 2020 in China, zero-COVID lockdown measures aimed to contain and eliminate the spread of the virus. This paper examines the associated policy responses around urban food security in early 2020, with a particular focus on two cities: Wuhan (where SARS-CoV-2 was first identified) and Nanjing (a neighbouring city). The analysis is based on an inventory of policy-related documents providing a wide range of information about governance responses to the pandemic. Four major governance challenges are addressed: agricultural production, food transportation, stabilization of food prices, and new contactless methods in purchasing foods. Key recommendations for post-pandemic policy responses around urban food security include: ensuring consistency throughout all levels of government, strengthening existing food reserves to leverage emergency responses, addressing the root causes of pandemic-related food insecurity by focusing on access at the household level, and improving food utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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