Age, Gender, Education, Political Orientation, and Animal Identification Predict Adoption of Meat Alternatives in a Representative Sample
Bibliographic record
Abstract
The current study aimed to identify the sociodemographic and psychological factors that predict an increased willingness to eat cultured meat and plant-based meat alternatives, as well as the consumption of plant-based meat alternatives. A cross-sectional questionnaire survey was conducted online among a representative sample of Canadian adults (n = 1,069). Descriptive analyses revealed that the majority of the respondents were either willing or uncertain about trying cultured meat and plant-based meat alternatives, while a minority were not willing to try these alternatives to meat. In terms of sociodemographic factors, being younger, of a more left-leaning political orientation, and having attained a higher education level predicted a greater adoption of meat alternatives. While men reported being more willing to eat cultured meat, women reported consuming a greater number of portions of plant-based meat alternatives. The psychological variable pertaining to our connection with other animals, namely identification with animals, and its dimension of human–animal similarity more specifically, predicted higher willingness to eat cultured meat and plant-based meat alternatives. Our findings confirm the role of age, gender, education, and political orientation in predicting the adoption of meat alternatives in a representative Canadian sample. They also show that feeling more similar to animals predicts these timely outcomes.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".