Public Perceptions Regarding Genomic Technologies Applied to Breeding Farm Animals: A Qualitative Study
Bibliographic record
Abstract
The societal acceptability of different applications of genomic technologies to animal production systems will determine whether their innovation trajectories will reach the commercialisation stage. Importantly, technological implementation and commercialisation trajectories, regulation, and policy development need to take account of public priorities and attitudes. More effective co-production practices will ensure the application of genomic technologies to animals aligns with public priorities and are acceptable to society. Consumer rejection of, and limited demand for, animal products developed using novel genomic technologies will determine whether they are integration into the food system. However, little is known about whether genomic technologies that accelerate breeding but do not introduce cross-species genetic changes are more acceptable to consumers than those that do. Five focus groups, held in the north east of England, were used to explore the perceptions of, and attitudes towards, the use of genomic technologies in breeding farm animals for the human food supply chain. Overall, study participants were more positive towards genomic technologies applied to promote animal welfare (e.g., improved disease resistance), environmental sustainability, and human health. Animal "disenhancement" was viewed negatively and increased food production alone was not perceived as a potential benefit. In comparison to gene editing, research participants were most negative about genetic modification and the application of gene drives, independent of the benefits delivered.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".