CANADIAN CONSUMER PERCEPTION OF GENOME-EDITED FOOD PRODUCTS
Bibliographic record
Abstract
New Breeding techniques (NBTs) have been developed in the last decade and allow for faster, more precise and less expensive genetic modification of new plant varieties with desired traits. Genome editing technology is potentially more socially acceptable than transgenics due to the possibility to add, delete, or alter specific parts of the DNA sequence without adding foreign genetic material. This thesis examines consumers’ perceptions of food produced using genome editing techniques. To accomplish this, an online survey was administered across Canada, resulting in a sample of 503 participants. Econometric analysis was used to examine the relationship between consumers’ perceptions of food produced using genome editing technology and consumer preferences. Additional analysis was conducted for the other two food technologies (transgenics and organic) and results were compared. Results suggest that surveyed Canadians have better perceptions of genome editing technology and four factors are relevant to predict consumers’ levels of perception: trust in Canada’s food safety system, their food technology neophobia score, knowledge of genetics, and self-rated knowledge of genome editing. Food technology neophobia scores and knowledge both impact willingness to consume genome-edited and transgenics food products but not organic food products.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".