Social acceptance of genetic engineering technology
Bibliographic record
Abstract
Genetic engineering of animals has been proposed to address societal problems, but public acceptance of the use of this technology is unclear. Previous work has shown that the source of information proposing the technology (e.g. companies, universities), the term used to describe the technology (e.g. genome editing, genetic modification), and the genetic engineering application (e.g. different food products) affects technology acceptance. We conducted three mixed-method surveys and used a causal trust-acceptability model to understand social acceptance of genetic engineering (GE) by investigating 1) the source of information proposing the technology, 2) the term used to describe the technology, and 3) the GE application for farm animals proposed. Further, participants expressed their understanding of technology using a range of terms interchangeably, all describing technology used to change an organism's DNA. We used structural equation modelling and confirmed model fit for each survey. In each survey, perceptions of benefit had the greatest effect on acceptance. Following our hypothesized model, social trust had an indirect influence on acceptance through similar effects of perceived benefit and perceived risk. Additional quantitative analysis showed that the source of information and technology term had little to no effect on acceptance. Applications involving animals were perceived as less beneficial than a plant application, and an application for increased cattle muscle growth was perceived as more risky than a plant application. When assessing the acceptability of applications participants considered impacts on plants, animals, and people, trust in actors and technologies, and weighed benefits and drawbacks of GE. Future work should consider how to best measure acceptability of GE for animals, consider contextual factors and consider the use of inductive frameworks.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".