Avoid is Better than Generate: The Effect of Framing Information on Consumer Preferences and Willingness to Pay for Plant-Based Milk
Bibliographic record
Abstract
Agricultural and food systems play a crucial role in affecting climate change, and shifting towards plant-based diets has been recognized as a beneficial strategy to reduce environmental pressures. A stated choice study was conducted to better understand consumers’ interest and motives toward consuming alternative plant-based beverages, particularly the way that information is communicated to consumers. We collected 1825 online survey responses in Canada and 1865 survey responses in China using panels accessed through market research companies. Te results confirm the positive impact of GHG information exposure and highlight the importance of information framing. In both countries, the “avoid” framing has a stronger influence on the probability of choosing beverages with lower GHG emissions. Additionally, we find that some respondents strongly prefer products consistent with traditional dietary patterns, highlighting the potential difficulty of promoting dietary transitions, such as plant-based diets, in different contexts. These findings contribute to the understanding of consumer behavior and provide guidance for the development of sustainable consumption strategies.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".