How Do Cultural Worldviews Shape Food Technology Perceptions? Evidence from a Discrete Choice Experiment
Bibliographic record
Abstract
Abstract Agricultural biotechnology (genetic modification) has encountered resistance from many consumers, resulting in disparate regulatory approaches across different jurisdictions. The recent advent of CRISPR‐Cas9, or gene editing, offers the potential for significant improvements in plant breeding. However, little is known currently about consumer responses to the technology. A factor often omitted from previous economic analyses of consumer acceptance of new food technologies is underlying human values or worldviews. Drawing upon cultural cognition theory and using data from a survey of Canadian consumers, we examine the influence of cultural values on food choice behaviours. Respondents’ pre‐existing cultural values are measured on two dimensions: hierarchy‐egalitarianism and individualism‐communitarianism. Choice behaviours are captured using a discrete choice experiment featuring a sliced apple product with two consumer‐oriented attributes (non‐browning and antioxidant‐enhanced) and three novel food technologies (gene editing, genetic modification, edible coating). Using a random parameters logit model with error components we find pre‐existing cultural values to be significant determinants of choice behaviours. Individuals pre‐disposed towards a hierarchical worldview are more accepting of novel food technologies, as are individuals with a communitarian worldview. While the use of gene editing results in negative marginal utilities in a food choice situation, the effect is not as large as with genetic modification, suggesting there is scope to ameliorate potentially negative reactions to the technology with value‐compatible messages.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".