Pathways to food insecurity: Migration, hukou and COVID‐19 in 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 COVID-19 pandemic has issued significant challenges to food systems and the food security of migrants in cities. In China, there have been no studies to date focusing on the food security of migrants during the pandemic. To fill this gap, an online questionnaire survey of food security in Nanjing City, China, was conducted in March 2020. This paper situates the research findings in the general literature on the general migrant experience during the pandemic under COVID and the specifics of the Chinese policy of hukou. Using multiple linear regression and ordered logistic regression, the paper examines the impact of migration status on food security during the pandemic. The paper finds that during the COVID-19 outbreak in 2020, households without local Nanjing hukou were more food insecure than those with Nanjing hukou. The differences related more to the absolute quantity of food intake, rather than reduction in food quality or in levels of anxiety over food access. Migrants in China and elsewhere during COVID-19 experienced three pathways to food insecurity-an income gap, an accessibility gap, and a benefits gap. This conceptual framework is used to structure the discussion and interpretation of survey findings and also has wider potential applicability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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