Moral Opposition to Genetically Engineered Food in the United States, France, and Germany
Bibliographic record
Abstract
When people are morally opposed to a practice, they often profess to be consequence-insensitive-that is, they say that they think it ought to be prohibited regardless of the risks and benefits. We investigate consequence-insensitive opposition to genetically engineered (GE) food in France, Germany, and the United States. Using nationally representative samples (total N = 1599), we find that most GE food opponents in all three countries are consequence-insensitive (France: 93.1%; Germany: 87.4%; United States: 81.3%). Consequence-insensitive opponents differ from other opponents in other ways consistent with their holding moral beliefs. They are more likely to display other properties of sacred moral values, like quantity insensitivity and universalism. They also see GE food as more personally important, are less willing to consume it, are more in favor of policies restricting it, and are more willing to engage in activism against it.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".