“Frequently Asked Questions” About Genetic Engineering in Farm Animals: A Frame Analysis
Bibliographic record
Abstract
Abstract Calls for public engagement on emerging agricultural technologies, including genetic engineering of farm animals, have resulted in the development of information that people can interact and engage with online, including “Frequently Asked Questions” (FAQs) developed by organizations seeking to inform or influence the debate. We conducted a frame analysis of FAQs webpages about genetic engineering of farm animals developed by different organizations to describe how questions and answers are presented. We categorized FAQs as having a regulatory frame (emphasizing or challenging the adequacy of regulations), an efficiency frame (emphasizing precision and benefits), a risks and uncertainty frame (emphasizing unknown outcomes), an animal welfare frame (emphasizing benefits for animals) or an animal dignity frame (considering the inherent value of animals). Animals were often featured as the object of regulations in FAQs, and questions about animals were linked to animal welfare regulations. The public were represented using a variety of terms (public, consumer) and pronouns (I, we). Some FAQs described differences between technology terms (gene editing, genetic modification) and categorized technologies as either well-established or novel. This framing of the technology may not respond to actual public concerns on the topic. Organizations seeking to use FAQs as a public engagement tool might consider including multiple viewpoints and actual questions people have about genetic engineering.
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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 | 0.038 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".