Public attitudes toward the use of technology to create new types of animals and animal products
Bibliographic record
Abstract
Philosophers have used thought experiments to examine contentious examples of genetic modification. We hypothesised that these examples would prove useful in provoking responses from lay participants concerning technological interventions used to address welfare concerns. We asked 747 US and Canadian citizens to respond to two scenarios based on these thought experiments: genetically modifying chickens to produce blind progeny that are less likely to engage in feather-pecking (BC); and genetically modifying animals to create progeny that do not experience any subjective state (i.e. incapable of experiencing pain or fear; IA). For contrast, we assessed a third scenario that also resulted in the production of animal protein with no risk of suffering but did not involve genetically modifying animals: the development of cultured meat (CM). Participants indicated on a seven-point scale how acceptable they considered the technology (1 = very wrong to do; 7 = very right to do), and provided a text-based, open-ended explanation of their response. The creation of cultured meat was judged more acceptable than the creation of blind chickens and insentient animals. Qualitative responses indicated that some participants accepted the constraints imposed by the thought experiment, for example, by accepting perceived harms of the technology to achieve perceived benefits in reducing animal suffering. Others expressed discomfort with such trade-offs, advocating for other approaches to reducing harm. We conclude that people vary in their acceptance of interventions within existing systems, with some calling for transformational change.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".