Public attitudes toward the use of technology to create new types of animals and animal products
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it