Gene Editing for Improved Animal Welfare and Production Traits in Cattle: Will This Technology Be Embraced or Rejected by the Public?
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
Integrating technology into agricultural systems has gained considerable traction, particularly over the last half century. Agricultural systems that incorporate the public’s concerns regarding farm animal welfare are more likely to be socially accepted in the long term, a key but often forgotten component of sustainability. Gene editing is a tool that has received considerable attention in the last five years, given its potential capacity to improve farm animal health, welfare, and production efficiency. This study aimed to explore the attitudes of Brazilian citizens regarding the applications of gene editing in cattle that generate offspring without horns; are more resistant to heat; and have increased muscle tissue. Using a mixed-methods approach, we surveyed participants via face-to-face, using in-depth interviews (Study 1) and an online questionnaire containing closed-ended questions (Study 2). Overall, the acceptability of gene editing was low and in cases where support was given it was highly dependent on the type and purpose of the application proposed. Using gene editing to improve muscle tissue growth was viewed as less acceptable compared to using gene editing to reduce heat stress or to produce hornless cattle. Support declined when the application was perceived to harm animal welfare, to be profit motivated or to reinforce the status quo of intensive livestock systems. The acceptability of gene editing was reduced when perceptions of risks and benefits were viewed as unevenly or unfairly distributed among consumers, corporations, different types of farmers, and the animals. Interviewees did not consider gene editing a “natural” process, citing dissenting reasons such as the high degree of human interference and the acceleration of natural processes. Our findings raised several issues that may need to be addressed for gene editing to comply with the social pillar of sustainable agriculture.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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