Biotechnology in food
Bibliographic record
Abstract
Purpose The purpose of this paper is to measure Canadian attitudes towards genetic engineering in food, for both plant-based and livestock, assess trust towards food safety and overall regulatory system in Canada. Design/methodology/approach This exploratory study is derived from an inductive, quantitative analysis of primary data obtained from an online survey of adults, aged 18 and over, living in Canada for at least 12 months. An online survey was widely distributed in both French and English. Data were collected from 1,049 respondents. The sample was randomized using regional and demographic benchmarks for an accurate representation of the Canadian population. The completion rate of the survey was 94 per cent. Based on the sampling design, the margin of error is 3.1 per cent, 19 times out of 20. Findings Consumers misunderstand the nature of genetic engineering or do not appreciate its prevalence in agrifood or both. In total, 44 per cent of Canadians are confused about health effects of genetically engineered foods and ingredients. In total, 40 per cent believe that there is not significant testing on genetically engineered food to protect consumers. In total, 52 per cent are uncertain on their consumption of genetically engineered food, despite its prominence in the agrifood marketplace. Scientific literacy of respondents on genetic engineering is low. While Canadians are divided on purchasing genetically engineered animal-based products, 55 per cent indicated price is the most important factor when purchasing food. Research limitations/implications More research is required to better appreciate the sociological and economic dimensions of incorporating GM foods into our lives. Most importantly, longitudinal risks ought to be better understood for both plant- and animal-based GM foods and ingredients. Additional research is needed to quantify the benefits and risks of GM crops livestock, so business practices and policies approach market expectations. Significantly, improving consumers’ scientific literacy on GM foods will reduce confusion and allow for more informed purchasing decisions. Indeed, a proactive research agenda on biotechnologies can accommodate well-informed discussions with public agencies, food businesses and consumers. Originality/value This exploratory study is one of the first to compare consumers’ perceptions of genetic engineering related to animal and plant-based species in Canada since the addition of genetically modified salmon to the marketplace.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".