Attachment to Meat and Willingness Towards Cultured Alternatives Among Consumers: A Cross-Sectional Study in the UAE
Bibliographic record
Abstract
Background/Objectives: The escalating global demand for meat, as a sequela of population growth, has led to unsustainable livestock production, resulting in a host of environmental and food security concerns. Various strategies have been explored to mitigate these issues, including the introduction of a novel food product, cultured meat. Cultured meat is not yet commercially available, yet public perceptions are already taking shape. To better understand the factors influencing its adoption by consumers, a cross-sectional, web-based study was conducted to examine consumer attitudes toward conventional meat and cultured meat among adults in the United Arab Emirates (UAE). Methods: The survey was conducted between December 2023 and March 2024 and used a convenience snowball sampling method. The questionnaire focused on current meat consumption patterns, meat attachment, and willingness to consume cultured meat. Sociodemographic data, including age, sex, education, and self-reported weight and height, were also collected. Results: Results showed that the vast majority (86%) of participants consumed all types of meats, while more than half (59.3%) were unfamiliar with the term “cultured meat”. Despite this unfamiliarity, about one-third (35%) were somewhat willing to try cultured meat, though more than two-thirds (69%) were reluctant to replace conventional meat with cultured meat in their diet. Male participants and those with higher BMIs showed a significantly stronger attachment to conventional meat. Willingness to consume cultured meat was notably higher among participants aged less than 30 years, those having less formal education, and those who are Arabic. Conclusions: These findings suggest that while interest in cultured meat exists, significant barriers remain, particularly regarding consumer education and cultural acceptance.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".