Chinese consumers’ preference for selected food safety attributes of milk powders
Bibliographic record
Abstract
A series of milk safety scandals have occurred in China since the early 2000s that not only led to thousands of consumers falling ill, but also caused the deaths of infants. The milk scandals scared many consumers in mainland China away from domestic dairy products. Foreign branded dairy products, especially baby formulas, have become increasingly popular in China. Current little research has been dedicated to analyzing Chinese consumers’ preference for selected milk powder attributes such as “Hazard Analysis and Critical Control Point (HACCP)” and “Organic.” This study utilized an in-person interview of 1,404 respondents across 18 different locations in the Chinese cities of Beijing and Zhengzhou to study Chinese consumers’ preference for “Traceability”, “Direct Ownership of Farms”, “Country-of-origins”, “Farming Method (Organic vs. Conventional)” and “Safety Production Standards (Hazard Analysis and Critical Control Points). A Mixed Logit Model was used to estimate consumers’ preference and willingness to pay for milk powder safety attributes. The research revealed that 64% Chinese consumers believe imported milk powders are safer than domestic milk powders. Consumers are willing to pay more milk powders with “Traceability” and “Direct ownership of farm” attributes. Consumers with better education and full-time employment are more likely to pay attention to the “Traceability” and “Direct ownership of farm” attributes of milk powder.
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.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".