Supplemental material for: Public acceptance of emerging biotechnologies in animal agriculture
Bibliographic record
Abstract
Biotechnologies are being developed in animal agriculture to address societal concerns related to disease, resistance to antimicrobials, and sustainability. Understanding public acceptance of these technologies and views about food produced using them is essential to inform technology development. We conducted a mixed-method survey with North American participants (n = 1,340) and used a hypothesized causal trust-acceptability model and open-ended questions to investigate acceptance of two biotechnologies: antimicrobial peptides (AMP) and gene editing (GE). Structural equation modeling showed that trust in institutions had the largest total effect on acceptance, acting primarily through indirect pathways of increased perceived benefit and reduced perceived risk, with a smaller but direct effect on acceptance. Participants had higher levels of trust in institutions and perceived AMP to be more beneficial, acceptable, and less risky compared to GE. In open-ended responses, participants wanted to know more about AMP and GE (e.g., purpose, mechanisms of action) and had specific questions related to biotechnology risks and safety, animal welfare, and human impacts. Participants argued that both biotechnologies could improve or harm animal welfare, weighed implications for disease management, and saw potential benefits for humans. Participants also discussed the naturalness of AMP and GE, and considered their level of trust in the institutions behind technology development. These results offer insight into views about biotechnologies in animal agriculture, can support technology developers and policy makers in evaluating which technologies to develop, and may improve communication between technology developers and publics.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.227 | 0.069 |
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".