An Overview of Attitudes Toward Genetically Engineered Food
Bibliographic record
Abstract
Genetically engineered food has had its DNA, RNA, or proteins manipulated by intentional human intervention. We provide an overview of the importance and regulation of genetically engineered food and lay attitudes toward it. We first discuss the pronaturalness context in the United States and Europe that preceded the appearance of genetically engineered food. We then review the definition, prevalence, and regulation of this type of food. Genetically engineered food is widespread in some countries, but there is great controversy worldwide among individuals, governments, and other institutions about the advisability of growing and consuming it. In general, life scientists have a much more positive view of genetically engineered food than laypeople. We examine the bases of lay opposition to genetically engineered food and the evidence for how attitudes change. Laypeople tend to see genetically engineered food as dangerous and offering few benefits. We suggest that much of the lay opposition is morally based. One possibility is that, in some contexts, people view nature and naturalness as sacred and genetically engineered food as a violation of naturalness. We also suggest that for many people these perceptions of naturalness and attitudes toward genetically engineered food follow the sympathetic magical law of contagion, in which even minimal contact between a natural food and an unnatural entity, either a scientist or a piece of foreign DNA, pollutes or contaminates the natural entity and renders it unacceptable or even immoral to consume.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".