Dietary Structural Change in China's Cities: Empirical Fact or Urban Legend?
Bibliographic record
Abstract
China's economic reforms, which began in 1978, resulted in remarkable income growth, and urban Chinese consumers have responded by dramatically increasing their consumption of meat, other livestock products and fruits and by decreasing consumption of grain‐based foods. Economic prosperity, a growing openness to international markets, and domestic policy reforms have changed the food marketing environment for Chinese consumers and may have contributed to shifts in consumer food demand. The objective of this paper is to uncover evidence of structural change in food consumption among urban residents in China. Both parametric and nonparametric methods are used to test for structural change in aggregate household data from 1981 to 2004. The tests provided a reasonably clear picture of changing food consumption over the study period. En Chine, les réformes économiques amorcées en 1978 ont entraîné une croissance remarquable du revenu à laquelle les consommateurs urbains chinois ont réagi en augmentant considérablement leur consommation de viande, d'autres produits d'élevage et de fruits et en diminuant leur consommation de produits alimentaires à base de céréales. La prospéritééconomique, une ouverture croissante sur les marchés internationaux et les réformes de la politique intérieure ont modifié l'environnement commercial des produits alimentaires des consommateurs chinois et peuvent avoir contribuéà modifier la demande des consommateurs pour des produits alimentaires. Le présent article visait à recueillir des preuves de changement structurel dans la consommation alimentaire des citadins chinois. Nous avons utilisé des méthodes paramétriques et non paramétriques pour tester les changements structurels à partir de données agrégées sur les foyers de 1981 à 2004. Les tests ont brossé un tableau assez clair de l'évolution de la consommation alimentaire au cours de la période à l'étude.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".