Stakeholder Views on the Creation and Use of Genetically-Engineered Animals in Research
Bibliographic record
Abstract
This interview-based study examined the diversity of views relating to the creation and use of genetically-engineered (GE) animals in biomedical science. Twenty Canadian participants (eight researchers, five research technicians and seven members of the public) took part in the interviews, in which four main themes were discussed: a) how participants felt about the genetic engineering of animals as a practice; b) governance of the creation and use of GE animals in research, and whether current guidelines are sufficient; c) the Three Rs (Replacement, Reduction, Refinement) and how they are applied during the creation and use of GE animals in research; and d) whether public opinion should play a greater role in the creation and use of GE animals. Most of the participants felt that the creation and use of GE animals for biomedical research purposes (as opposed to food purposes) is acceptable, provided that tangible human health benefits are gained. However, obstacles to Three Rs implementation were identified, and the participants agreed that more effort should be placed on engaging the public on the use of GE animals in research.
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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 | 0.069 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".