Perceptions and acceptance of yeast-derived dairy in British Columbia, Canada
Bibliographic record
Abstract
Yeast derived-dairy (YDD) produced using cellular agriculture technologies is already available for purchase in the United States, though there has been little study of public understanding of these products. Our pilot study explored consumer perception and acceptance of YDD and yeast-derived agriculture (YDA). The study employed a questionnaire consisting of Likert scale, multiple-choice and open-ended questions, which was disseminated to vegans and the food-interested public in the province of British Columbia, Canada. Quantitative data was analyzed using SPSS 27.0, and qualitative data was collected and analyzed (in English) using thematic analysis. A binary logistic regression model indicated that among our participants, being vegan or 35 years of age or older negatively predicted having positive feelings towards YDA [chi-square (10) = 29.086, p = 0.001]. Vegans were less likely to try or purchase YDD than non-vegans. Consumers in our study shared concerns regarding the health and safety of YDD with many viewing it as non-vegan and a highly processed product. Although vegans receive a disproportionate amount of media attention with regards to cellular agriculture, our pilot study suggests this group may be unlikely to accept or consume YDA or YDD. Rather, our preliminary work indicates non-vegans and individuals under the age of 35 may be a more receptive market. Across groups, confusion about YDA processes may be a barrier to adoption.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".