Analyzing consumer preferences and willingness-to-pay for cell-cultured beef: the impact of social media, subjective and objective knowledge, and political polarization among Canadian participants.
Bibliographic record
Abstract
Traditional livestock production has gained considerable attention for its environmental impact, particularly its land use and greenhouse gas production. Cell cultured meat, or meat produced in a lab from animal stem cells, has been proposed as a solution to meet consumer demand for meat products while reducing the environmental impacts of agricultural production. Past work shows that consumers are reluctant to choose cell cultured meat. A major driver of public skepticism towards novel foods may be distrust in messaging surrounding these products. We predict this sentiment towards cell cultured meat may be exacerbated by the proliferation of disinformation and social media use, resulting in high levels of mistrust of new food technologies. In this paper, we seek to understand how media bias, political polarization, and social media use influence consumers’ willingness to consume cultured meat. In a hypothetical choice experiment, Canadian participants were offered ground beef options. Participants were initially shown one of four information treatments comparing conventional, organic, and cultured meat: information highlighting the taste of meat, the process of meat production, animal welfare, or the environmental impact. A mixed-logit model predicts that, compared to conventional ground beef, consumers are willing to pay $1.15/lb less for cell cultured ground beef and $0.59/lb more for organic ground beef. However, the type of information participants received has no effect on WTP, apart from participants with high food technology neophobia, which showed a lower WTP for cell cultured meat when presented with information about the production process. Additionally, participants that are men and use social media as a main news source are more likely to choose the cell cultured ground beef over conventional ground beef. Additionally, trusting news from more news sources, high objective knowledge, and low subjective knowledge are associated with higher probability of choosing cell cultured ground beef over conventional ground beef. These results imply that mistrust of established media may contribute to the rejection of cell cultured meat. These findings point to a larger societal phenomenon of general mistrust in authority and present a potential challenge for future advertisers in seeking public trust and acceptance of this novel product
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".