Plant‐based meats in China: a cross‐sectional study of attitudes and behaviours
Bibliographic record
Abstract
BACKGROUND: This study investigated potential opportunities or challenges for plant-based meat in the Chinese market. A quantitative framework was applied to determine the current level of familiarity and experience with plant-based meat among Chinese consumers, the proportion of consumers who would try or purchase plant-based meat, which demographics within China are most likely to buy plant-based meat and which attitudes are important in driving the purchase intent of plant-based meat. METHODS: A pre-registered cross-sectional online survey (N = 1206) was distributed to respondents (matched to China's adult population for gender and age). RESULTS: Respondents reported a variety of dietary identities, with 43.4% reporting that they were reducing or avoiding meat. The majority of respondents (60.1%) said they had eaten plant-based meat at least once before. Of those who said they had never eaten plant-based meat, 41.9% intended to try it and 31.4% intended to purchase it. The strongest attitudinal predictor of plant-based meat purchase intent was perceived healthiness (β = 0.235, p < 0.001), whereas the strongest demographic predictor of plant-based meat purchase intent was age (β = -0.248, p < 0.001). CONCLUSIONS: The findings of this study suggest that an approach based on increasing opportunities for trial, as well as appealing to specific attitudinal and demographic predictors of plant-based purchase intent, could prove successful in increasing adoption of plant-based and alternative meat products.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".