Citizen views on genome editing: effects of species and purpose
Bibliographic record
Abstract
Abstract Public opinion can affect the adoption of genome editing technologies. In food production, genome editing can be applied to a wide range of applications, in different species and with different purposes. This study analyzed how the public responds to five different applications of genome editing, varying the species involved and the proposed purpose of the modification. Three of the applications described the introduction of disease resistance within different species (human, plant, animal), and two targeted product quality and quantity in cattle. Online surveys in Canada, the US, Austria, Germany and Italy were carried out with a total sample size of 3698 participants. Using a between-subject design, participants were confronted with one of the five applications and asked to decide whether they considered it right or wrong. Perceived risks, benefits, and the perception of the technology as tampering with nature were surveyed and were complemented with socio-demographics and a measure of the participants’ moral foundations. In all countries, participants evaluated the application of disease resistance in humans as most right to do, followed by disease resistance in plants, and then in animals, and considered changes in product quality and quantity in cattle as least right to do. However, US and Italian participants were generally more positive toward all scenarios, and German and Austrian participants more negative. Cluster analyses identified four groups of participants: ‘strong supporters’ who saw only benefits and little risks, ‘slight supporters’ who perceived risks and valued benefits, ‘neutrals’ who showed no pronounced opinion, and ‘opponents’ who perceived higher risks and lower benefits. This research contributes to understanding public response to applications of genome editing, revealing differences that can help guide decisions related to adoption of these technologies.
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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 | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".