Pathways to food insecurity: Migration, hukou and COVID‐19 in Nanjing, China
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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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".