Food safety in urban China: Perceptions and coping strategies of residents in Nanjing
Bibliographic record
Abstract
Food safety has become an increasingly pressing sociopolitical issue in China due to the outbreak of food safety scandals since the 2000s. Existing studies have highlighted the socio-economic context of this issue, its drivers and implications. Yet, few studies have examined the perceptions of food safety conditions and strategies undertaken by consumers in their daily lives to cope with the challenge. Based on a city-wide survey of 1210 households and 36 interviews in Nanjing, China, this research adopts an ‘everyday’ perspective of analysis to investigate Nanjing residents’ perceptions of, and strategies to cope with, the food safety challenge. Perceptions include the severity of the food safety problem, the least safe foods, as well as causes and responsibilities. Coping strategies include various approaches to food access and food preparation. This article also compares the validity of potential sources of trust in food. On the one hand, the study demonstrates how the structural changes in China’s food system (i.e. chemical intensive food production and elongated food supply chains) constitute the major problems and causes of food safety issues. On the other hand, it reveals the considerable latitude within which Nanjing residents proactively exercise their agency when facing food safety challenges.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".