Social Acceptability of Cisgenic Plants: Public Perception, Consumer Preferences, and Legal Regulation
Bibliographic record
Abstract
Abstract Part of the rationale behind the introduction of the term cisgenesis was the expectation that due to the “more natural” character of the genetic modification, cisgenic plants would be socially more acceptable than transgenic ones. This chapter assesses whether this expectation was justified. It thereby addresses three arenas of social acceptability: public perception, consumer preferences, and legal regulation. Discussing and comparing recent studies from four geographical areas across the globe—Europe, North America, Japan, and Australia and New Zealand—the chapter shows that the expectation was justified, and that cisgenic plants are treated as being more acceptable than other forms of genetic modification. Yet, there are considerable differences across the three arenas of social acceptability. In Australia, Canada, and the United States of America, the legal regulation of cisgenic plants is less restrictive than in Europe, Japan, and New Zealand. Also, the public perceptions are rather diverse across these countries, as are the factors that are deemed most influential in informing public opinion and consumer decisions. While people in North America appear to be most interested in individual benefits of the products (improved quality, health aspects), Europeans are more likely to accept cisgenic plants and derived products if they have a proven environmental benefit. In New Zealand, in contrast, the potential impact of cisgenic plants on other, more or less related markets, like meat export and tourism, is heavily debated. We conclude with some remarks about a possible new arrangement between science and policy that may come about with a new, or homogenized, international regulatory regime.
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".