Mitigating Consequence Insensitivity for Genetically Engineered Crops
Bibliographic record
Abstract
Many opponents of genetically engineered (GE) food say that it ought to be prohibited regardless of the risks and benefits (Scott et al., 2016). If many people are truly unwilling to consider risks and benefits in evaluating GE technology, this poses serious problems for scientists and policy-makers. In a large demographically-representative German sample (N = 3,025), we investigate consequence-insensitive beliefs about GE crops among GE supporters and opponents, as well as whether these beliefs can be mitigated. We find that a large majority of opponents and a substantial minority of supporters are consequence-insensitive: They say that risks and benefits are irrelevant to their views. At the same time, the responses of consequence-insensitive participants to subsequent belief probes show substantial flexibility. Participants often gave responses inconsistent with the unconditional prohibition or permission of GE crops. These results suggest that professed con-sequence insensitivity should be taken as an expression of a strong moral belief rather than as literal endorsement of policy.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".