Canadian consumer acceptance of gene‐edited versus genetically modified potatoes: A choice experiment approach
Bibliographic record
Abstract
Abstract In 2016, second‐generation genetically modified (GM) potatoes were approved for production and sale in Canada. In this study, we analyze how consumer acceptance of GM potatoes may be affected by various factors, including the trait introduced (i.e., the product benefits), the type of breeding technology used, and the developer of the potato using any technology. We conduct an online survey and use a stated choice experiment to collect data on consumer acceptance of GM and other potatoes in Canada. Random utility models are used to analyze the economic value consumers place on the various attributes of the potatoes. Our results show that consumers are willing to pay more for a health attribute (reduced acrylamide produced when potatoes are fried) and an environmental attribute. Respondents in general need to face discounted prices to buy potatoes created by either gene editing or GM (either transgenic or cisgenic/intragenic) technologies. However, consumers are in general more accepting of the gene editing technology than the GM technologies. Our results also show that government is the most preferred developer of the potatoes, regardless of technology. Results from this study can help guide public and private management of the introduction of new foods when the products are developed with unpopular technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".