Using animal history to inform current debates in gene editing farm animals: A systematic review
Bibliographic record
Abstract
There is growing interest in gene editing farm animals. Some alterations could benefit animal welfare (e.g., improved heat tolerance in cattle with the “slick” gene), the environment (e.g., reducing methane emissions from cattle with induced pluripotent stem cells), and productivity (e.g., higher weight gains in cattle with the “double muscling” gene). Existing scholarship on the acceptability of such modifications has used myriad approaches to identify societal factors that shape the ethics and governance of this technology. We argue that integrating historical approaches—particularly from the relatively new and burgeoning field of animal history—offers a form of “anticipatory knowledge” that can help guide discussions on this topic. We conducted a systematic review of the animal history literature in English, German, and Spanish to identify the influence of political, scientific, economic, social, and cultural factors on the development and acceptance of such technologies. We identified analogous structures and fault lines in past debates about farm animals that provide insights for contemporary discussions about gene editing. Those analogous structures include the market power of meatpackers or the racialized precepts in livestock breeding, and fault lines, like the disconnect between states and citizens over the direction of food systems. Highlighting these similarities demonstrates how external forces have shaped—and will continue to shape—the acceptance or rejection of emerging biotechnologies as applied to farm animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".